def setup_class(cls):
cls.rng = RandomState(12345)
data = macrodata.load().data
cls.cpi = log(data['cpi'])
cls.realgdp = data['realgdp']
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
def setup_class(cls):
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
cls.res1 = res_ols = OLS(g_inv, exogg).fit()
def test_hac_simple():
from statsmodels.datasets import macrodata
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]],prepend=True)
res_olsg = OLS(g_inv, exogg).fit()
#> NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE, verbose=TRUE, adjust=TRUE)
#Lag truncation parameter chosen: 4
# (Intercept) ggdp lint
cov1_r = [[ 1.40643899878678802, -0.3180328707083329709, -0.060621111216488610],
[ -0.31803287070833292, 0.1097308348999818661, 0.000395311760301478],
[ -0.06062111121648865, 0.0003953117603014895, 0.087511528912470993]]
#> NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE, verbose=TRUE, adjust=FALSE)
#Lag truncation parameter chosen: 4
# (Intercept) ggdp lint
cov2_r = [[ 1.3855512908840137, -0.313309610252268500, -0.059720797683570477],
[ -0.3133096102522685, 0.108101169035130618, 0.000389440793564339],
[ -0.0597207976835705, 0.000389440793564336, 0.086211852740503622]]
cov1, se1 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=True)
cov2, se2 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False)
assert_almost_equal(cov1, cov1_r, decimal=14)
assert_almost_equal(cov2, cov2_r, decimal=14)
def notyet_atst():
d = macrodata.load().data
realinv = d['realinv']
realgdp = d['realgdp']
realint = d['realint']
endog = realinv
exog = add_constant(np.c_[realgdp, realint],prepend=True)
res_ols1 = OLS(endog, exog).fit()
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
lint = d['realint'][:-1]
tbilrate = d['tbilrate'][:-1]
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, lint], prepend=True)
exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate], prepend=True)
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
#the following were done accidentally with res_ols1 in R,
#with original Greene data
params = np.array([-272.3986041341653, 0.1779455206941112,
0.2149432424658157])
cov_hac_4 = np.array([1321.569466333051, -0.2318836566017612,
37.01280466875694, -0.2318836566017614, 4.602339488102263e-05,
-0.0104687835998635, 37.012804668757, -0.0104687835998635,
21.16037144168061]).reshape(3,3, order='F')
cov_hac_10 = np.array([2027.356101193361, -0.3507514463299015,
54.81079621448568, -0.350751446329901, 6.953380432635583e-05,
-0.01268990195095196, 54.81079621448564, -0.01268990195095195,
22.92512402151113]).reshape(3,3, order='F')
#goldfeld-quandt
het_gq_greater = dict(statistic=13.20512768685082, df1=99, df2=98,
pvalue=1.246141976112324e-30, distr='f')
het_gq_less = dict(statistic=13.20512768685082, df1=99, df2=98, pvalue=1.)
het_gq_2sided = dict(statistic=13.20512768685082, df1=99, df2=98,
pvalue=1.246141976112324e-30, distr='f')
#goldfeld-quandt, fraction = 0.5
het_gq_greater_2 = dict(statistic=87.1328934692124, df1=48, df2=47,
pvalue=2.154956842194898e-33, distr='f')
gq = smsdia.het_goldfeldquandt(endog, exog, split=0.5)
compare_t_est(gq, het_gq_greater, decimal=(13, 14))
assert_equal(gq[-1], 'increasing')
harvey_collier = dict(stat=2.28042114041313, df=199,
pvalue=0.02364236161988260, distr='t')
#hc = harvtest(fm, order.by=ggdp , data = list())
harvey_collier_2 = dict(stat=0.7516918462158783, df=199,
pvalue=0.4531244858006127, distr='t')
def test_stata_writer_structured():
buf = BytesIO()
dta = macrodata.load().data
dtype = dta.dtype
dta = dta.astype(
np.dtype([('year', int), ('quarter', int)] + dtype.descr[2:]))
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
assert_array_equal(dta, dta2)
def setup_class(self):
d2 = macrodata.load().data
g_gdp = 400 * np.diff(np.log(d2['realgdp']))
g_inv = 400 * np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
mod1 = GLSAR(g_inv, exogg, 1)
self.res = mod1.iterative_fit(5)
from .results.macro_gr_corc_stata import results
self.results = results
def test_stata_writer_array():
buf = BytesIO()
dta = macrodata.load(as_pandas=False).data
dta = DataFrame.from_records(dta)
dta.columns = ["v%d" % i for i in range(1, 15)]
writer = StataWriter(buf, dta.values)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
dta = dta.to_records(index=False)
assert_array_equal(dta, dta2)
def test_stata_writer_structured():
buf = BytesIO()
dta = macrodata.load().data
dtype = dta.dtype
dta = dta.astype(np.dtype([('year', int),
('quarter', int)] + dtype.descr[2:]))
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
assert_array_equal(dta, dta2)
def setup_class(self):
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
mod1 = GLSAR(g_inv, exogg, 1)
self.res = mod1.iterative_fit(5)
from results.macro_gr_corc_stata import results
self.results = results
def test_stata_writer_array():
buf = BytesIO()
dta = macrodata.load().data
dta = DataFrame.from_records(dta)
dta.columns = ["v%d" % i for i in range(1,15)]
writer = StataWriter(buf, dta.values)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
dta = dta.to_records(index=False)
assert_array_equal(dta, dta2)
def test_grangercausality(self):
# some example data
mdata = macrodata.load().data
mdata = mdata[['realgdp', 'realcons']]
data = mdata.view((float, 2))
data = np.diff(np.log(data), axis=0)
#R: lmtest:grangertest
r_result = [0.243097, 0.7844328, 195, 2] # f_test
gr = grangercausalitytests(data[:, 1::-1], 2, verbose=False)
assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7)
assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'], decimal=7)
def test_grangercausality(self):
# some example data
mdata = macrodata.load().data
mdata = recarray_select(mdata, ['realgdp', 'realcons'])
data = mdata.view((float, 2))
data = np.diff(np.log(data), axis=0)
#R: lmtest:grangertest
r_result = [0.243097, 0.7844328, 195, 2] # f_test
gr = grangercausalitytests(data[:, 1::-1], 2, verbose=False)
assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7)
assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'], decimal=7)
class CheckCorrGram(object):
"""
Set up for ACF, PACF tests.
"""
data = macrodata.load()
x = data.data['realgdp']
filename = os.path.dirname(os.path.abspath(__file__))+\
"/results/results_corrgram.csv"
results = genfromtxt(open(filename, "rb"),
delimiter=",",
names=True,
dtype=float)
def main():
"""
Toy program to test the KPSS autolag method of Hobijn et al (1998).
Unit tests using statsmodels data sets verified against SAS 9.3. To
use, modify the following lines in main KPSS method:
old:
if lags is None:
# from Kwiatkowski et al. referencing Schwert (1989)
lags = int(np.ceil(12. * np.power(nobs / 100., 1 / 4.)))
new:
if lags is None:
# autolag method of Hobijn et al. (1998)
lags = _kpss_autolag(resids, nobs)
"""
print("KPSS autolag method of Hobijn et al. (1998)")
# real GDP from macrodata data set
with warnings.catch_warnings(record=True) as w:
res = kpss(macrodata.load().data['realgdp'], 'c')
print(" realgdp('c'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 2.06851, decimal=3)
assert_equal(res[2], 9)
# sunspot activity from sunspots data set
with warnings.catch_warnings(record=True) as w:
res = kpss(sunspots.load().data['SUNACTIVITY'], 'c')
print(" sunactivity('c'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.66987, decimal=3)
assert_equal(res[2], 7)
# volumes from nile data set
with warnings.catch_warnings(record=True) as w:
res = kpss(nile.load().data['volume'], 'c')
print(" volume('c'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.86912, decimal=3)
assert_equal(res[2], 5)
# log-coinsurance from randhie data set
with warnings.catch_warnings(record=True) as w:
res = kpss(randhie.load().data['lncoins'], 'ct')
print(" lncoins('ct'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.36762, decimal=3)
assert_equal(res[2], 75)
# in-vehicle time from modechoice data set
with warnings.catch_warnings(record=True) as w:
res = kpss(modechoice.load().data['invt'], 'ct')
print(" invt('ct'): stat =", "{0:0.5f}".format(res[0]), " pval =",
"{0:0.5f}".format(res[1]), " lags =", format(res[2]))
assert_almost_equal(res[0], 0.40258, decimal=3)
assert_equal(res[2], 18)
def test_stata_writer_structured():
buf = BytesIO()
dta = macrodata.load(as_pandas=False).data
dtype = dta.dtype
dt = [('year', int), ('quarter', int)] + dtype.descr[2:]
if not PY3: # Remove unicode
dt = [(name.encode('ascii'), typ) for name, typ in dt]
dta = dta.astype(np.dtype(dt))
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
assert_array_equal(dta, dta2)
class CheckCoint(object):
"""
Test Cointegration Test Results for 2-variable system
Test values taken from Stata
"""
levels = ['1%', '5%', '10%']
data = macrodata.load()
y1 = data.data['realcons']
y2 = data.data['realgdp']
def test_tstat(self):
assert_almost_equal(self.coint_t, self.teststat, DECIMAL_4)
def test_stata_writer_structured():
buf = BytesIO()
dta = macrodata.load(as_pandas=False).data
dtype = dta.dtype
dt = [('year', int), ('quarter', int)] + dtype.descr[2:]
if not PY3: # Remove unicode
dt = [(name.encode('ascii'), typ) for name, typ in dt]
dta = dta.astype(np.dtype(dt))
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
assert_array_equal(dta, dta2)
def test_stata_writer_pandas():
buf = BytesIO()
dta = macrodata.load().data
dtype = dta.dtype
#as of 0.9.0 pandas only supports i8 and f8
dta = dta.astype(np.dtype([('year', 'i8'),
('quarter', 'i8')] + dtype.descr[2:]))
dta = DataFrame.from_records(dta)
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
ptesting.assert_frame_equal(dta.reset_index(), DataFrame.from_records(dta2))
def test_adf_autolag():
#see issue #246
#this is mostly a unit test
d2 = macrodata.load().data
for k_trend, tr in enumerate(['nc', 'c', 'ct', 'ctt']):
#[None:'nc', 0:'c', 1:'ct', 2:'ctt']
x = np.log(d2['realgdp'])
xd = np.diff(x)
#check exog
adf3 = tsast.adfuller(x,
maxlag=None,
autolag='aic',
regression=tr,
store=True,
regresults=True)
st2 = adf3[-1]
assert_equal(len(st2.autolag_results), 15 + 1) #+1 for lagged level
for l, res in sorted(iteritems(st2.autolag_results))[:5]:
lag = l - k_trend
#assert correct design matrices in _autolag
assert_equal(res.model.exog[-10:, k_trend], x[-11:-1])
assert_equal(res.model.exog[-1, k_trend + 1:], xd[-lag:-1][::-1])
#min-ic lag of dfgls in Stata is also 2, or 9 for maic with notrend
assert_equal(st2.usedlag, 2)
#same result with lag fixed at usedlag of autolag
adf2 = tsast.adfuller(x, maxlag=2, autolag=None, regression=tr)
assert_almost_equal(adf3[:2], adf2[:2], decimal=12)
tr = 'c'
#check maxlag with autolag
adf3 = tsast.adfuller(x,
maxlag=5,
autolag='aic',
regression=tr,
store=True,
regresults=True)
assert_equal(len(adf3[-1].autolag_results), 5 + 1)
adf3 = tsast.adfuller(x,
maxlag=0,
autolag='aic',
regression=tr,
store=True,
regresults=True)
assert_equal(len(adf3[-1].autolag_results), 0 + 1)
def test_stata_writer_structured():
buf = BytesIO()
dta = macrodata.load(as_pandas=False).data
dtype = dta.dtype
dt = [('year', int), ('quarter', int)] + dtype.descr[2:]
dta = dta.astype(np.dtype(dt))
with pytest.warns(FutureWarning):
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
with pytest.warns(FutureWarning):
dta2 = genfromdta(buf)
assert_array_equal(dta, dta2)
def setup_class(cls):
np.random.seed(12345)
t = 1100
y = np.zeros(t)
e = np.random.randn(t)
y[:2] = e[:2]
for i in range(3, t):
y[i] = 1.5 * y[i - 1] - 0.8 * y[i - 2] + 0.2 * y[i - 3] + e[i]
cls.y = y[100:]
cls.x = cls.y.std() * np.random.randn(t, 2)
cls.x = cls.x[100:]
cls.z = cls.y + cls.x.sum(1)
cls.cpi = log(macrodata.load().data['cpi'])
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
def setup_class(cls):
cls.rng = RandomState(12345)
t = 1100
y = np.zeros(t)
e = cls.rng.randn(t)
y[:2] = e[:2]
for i in range(3, t):
y[i] = 1.5 * y[i - 1] - 0.8 * y[i - 2] + 0.2 * y[i - 3] + e[i]
cls.y = y[100:]
cls.x = cls.y.std() * cls.rng.randn(t, 2)
cls.x = cls.x[100:]
cls.z = cls.y + cls.x.sum(1)
cls.cpi = log(macrodata.load().data['cpi'])
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
def test_bking1d():
"""
Test Baxter King band-pass filter. Results are taken from Stata
"""
bking_results = array([7.320813, 2.886914, -6.818976, -13.49436,
-13.27936, -9.405913, -5.691091, -5.133076, -7.273468,
-9.243364, -8.482916, -4.447764, 2.406559, 10.68433,
19.46414, 28.09749, 34.11066, 33.48468, 24.64598, 9.952399,
-4.265528, -12.59471, -13.46714, -9.049501, -3.011248,
.5655082, 2.897976, 7.406077, 14.67959, 18.651, 13.05891,
-2.945415, -24.08659, -41.86147, -48.68383, -43.32689,
-31.66654, -20.38356, -13.76411, -9.978693, -3.7704, 10.27108,
31.02847, 51.87613, 66.93117, 73.51951, 73.4053, 69.17468,
59.8543, 38.23899, -.2604809, -49.0107, -91.1128, -112.1574,
-108.3227, -86.51453, -59.91258, -40.01185, -29.70265,
-22.76396, -13.08037, 1.913622, 20.44045, 37.32873, 46.79802,
51.95937, 59.67393, 70.50803, 81.27311, 83.53191, 67.72536,
33.78039, -6.509092, -37.31579, -46.05207, -29.81496, 1.416417,
28.31503,
32.90134, 8.949259, -35.41895, -84.65775, -124.4288, -144.6036,
-140.2204, -109.2624, -53.6901, 15.07415, 74.44268, 104.0403,
101.0725, 76.58291, 49.27925, 36.15751, 36.48799, 37.60897,
27.75998, 4.216643, -23.20579, -39.33292, -36.6134, -20.90161,
-4.143123, 5.48432, 9.270075, 13.69573, 22.16675, 33.01987,
41.93186, 47.12222, 48.62164, 47.30701, 40.20537, 22.37898,
-7.133002, -43.3339, -78.51229, -101.3684, -105.2179,
-90.97147,
-68.30824, -48.10113, -35.60709, -31.15775, -31.82346,
-32.49278, -28.22499, -14.42852, 10.1827, 36.64189, 49.43468,
38.75517, 6.447761, -33.15883, -62.60446, -72.87829, -66.54629,
-52.61205, -38.06676, -26.19963, -16.51492, -7.007577,
.6125674,
7.866972, 14.8123, 22.52388, 30.65265, 39.47801, 49.05027,
59.02925,
72.88999, 95.08865, 125.8983, 154.4283, 160.7638, 130.6092,
67.84406, -7.070272, -68.08128, -99.39944, -104.911,
-100.2372, -98.11596, -104.2051, -114.0125, -113.3475,
-92.98669, -51.91707, -.7313812, 43.22938, 64.62762, 64.07226,
59.35707, 67.06026, 91.87247, 124.4591, 151.2402, 163.0648,
154.6432])
X = macrodata.load().data['realinv']
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y,bking_results,4)
class CheckADF(object):
"""
Test Augmented Dickey-Fuller
Test values taken from Stata.
"""
levels = ['1%', '5%', '10%']
data = macrodata.load()
x = data.data['realgdp']
y = data.data['infl']
def test_teststat(self):
assert_almost_equal(self.res1[0], self.teststat, DECIMAL_5)
def test_pvalue(self):
assert_almost_equal(self.res1[1], self.pvalue, DECIMAL_5)
def test_critvalues(self):
critvalues = [self.res1[4][lev] for lev in self.levels]
assert_almost_equal(critvalues, self.critvalues, DECIMAL_2)
def test_GLSARlag():
#test that results for lag>1 is close to lag=1, and smaller ssr
from statsmodels.datasets import macrodata
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
mod1 = GLSAR(g_inv, exogg, 1)
res1 = mod1.iterative_fit(5)
mod4 = GLSAR(g_inv, exogg, 4)
res4 = mod4.iterative_fit(10)
assert_array_less(np.abs(res1.params / res4.params - 1), 0.03)
assert_array_less(res4.ssr, res1.ssr)
assert_array_less(np.abs(res4.bse / res1.bse) - 1, 0.015)
assert_array_less(np.abs((res4.fittedvalues / res1.fittedvalues - 1).mean()),
0.015)
assert_equal(len(mod4.rho), 4)
def test_GLSARlag():
#test that results for lag>1 is close to lag=1, and smaller ssr
from statsmodels.datasets import macrodata
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
mod1 = GLSAR(g_inv, exogg, 1)
res1 = mod1.iterative_fit(5)
mod4 = GLSAR(g_inv, exogg, 4)
res4 = mod4.iterative_fit(10)
assert_array_less(np.abs(res1.params / res4.params - 1), 0.03)
assert_array_less(res4.ssr, res1.ssr)
assert_array_less(np.abs(res4.bse / res1.bse) - 1, 0.015)
assert_array_less(np.abs((res4.fittedvalues / res1.fittedvalues - 1).mean()),
0.015)
assert_equal(len(mod4.rho), 4)
def test_hac_simple():
from statsmodels.datasets import macrodata
d2 = macrodata.load().data
g_gdp = 400 * np.diff(np.log(d2['realgdp']))
g_inv = 400 * np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]])
res_olsg = OLS(g_inv, exogg).fit()
#> NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE, verbose=TRUE, adjust=TRUE)
#Lag truncation parameter chosen: 4
# (Intercept) ggdp lint
cov1_r = [
[1.40643899878678802, -0.3180328707083329709, -0.060621111216488610],
[-0.31803287070833292, 0.1097308348999818661, 0.000395311760301478],
[-0.06062111121648865, 0.0003953117603014895, 0.087511528912470993]
]
#> NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE, verbose=TRUE, adjust=FALSE)
#Lag truncation parameter chosen: 4
# (Intercept) ggdp lint
cov2_r = [
[1.3855512908840137, -0.313309610252268500, -0.059720797683570477],
[-0.3133096102522685, 0.108101169035130618, 0.000389440793564339],
[-0.0597207976835705, 0.000389440793564336, 0.086211852740503622]
]
cov1 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=True)
se1 = sw.se_cov(cov1)
cov2 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False)
se2 = sw.se_cov(cov2)
assert_almost_equal(cov1, cov1_r, decimal=14)
assert_almost_equal(cov2, cov2_r, decimal=14)
# compare default for nlags
cov3 = sw.cov_hac_simple(res_olsg, use_correction=False)
cov4 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False)
assert_almost_equal(cov3, cov4, decimal=14)
def __init__(self):
d = macrodata.load().data
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
lint = d['realint'][:-1]
tbilrate = d['tbilrate'][:-1]
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, lint], prepend=True)
exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate], prepend=True)
exogg3 = add_constant(np.c_[gs_l_realgdp], prepend=True)
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
res_ols3 = OLS(endogg, exogg3).fit()
self.res = res_ols
self.res2 = res_ols2
self.res3 = res_ols3
self.endog = self.res.model.endog
self.exog = self.res.model.exog
def setup_class(cls):
d = macrodata.load().data
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
lint = d['realint'][:-1]
tbilrate = d['tbilrate'][:-1]
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, lint])
exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate])
exogg3 = add_constant(np.c_[gs_l_realgdp])
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
res_ols3 = OLS(endogg, exogg3).fit()
cls.res = res_ols
cls.res2 = res_ols2
cls.res3 = res_ols3
cls.endog = cls.res.model.endog
cls.exog = cls.res.model.exog
def __init__(self):
d = macrodata.load().data
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
lint = d['realint'][:-1]
tbilrate = d['tbilrate'][:-1]
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, lint])
exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate])
exogg3 = add_constant(np.c_[gs_l_realgdp])
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
res_ols3 = OLS(endogg, exogg3).fit()
self.res = res_ols
self.res2 = res_ols2
self.res3 = res_ols3
self.endog = self.res.model.endog
self.exog = self.res.model.exog
def setup_class(cls):
d = macrodata.load().data
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
lint = d['realint'][:-1]
tbilrate = d['tbilrate'][:-1]
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, lint])
exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate])
exogg3 = add_constant(np.c_[gs_l_realgdp])
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
res_ols3 = OLS(endogg, exogg3).fit()
cls.res = res_ols
cls.res2 = res_ols2
cls.res3 = res_ols3
cls.endog = cls.res.model.endog
cls.exog = cls.res.model.exog
def test_adf_autolag():
#see issue #246
#this is mostly a unit test
d2 = macrodata.load().data
for k_trend, tr in enumerate(['nc', 'c', 'ct', 'ctt']):
#[None:'nc', 0:'c', 1:'ct', 2:'ctt']
x = np.log(d2['realgdp'])
xd = np.diff(x)
#check exog
adf3 = tsast.adfuller(x, maxlag=None, autolag='aic',
regression=tr, store=True, regresults=True)
st2 = adf3[-1]
assert_equal(len(st2.autolag_results), 15 + 1) #+1 for lagged level
for l, res in sorted(st2.autolag_results.iteritems())[:5]:
lag = l-k_trend
#assert correct design matrices in _autolag
assert_equal(res.model.exog[-10:,k_trend], x[-11:-1])
assert_equal(res.model.exog[-1,k_trend+1:], xd[-lag:-1][::-1])
#min-ic lag of dfgls in Stata is also 2, or 9 for maic with notrend
assert_equal(st2.usedlag, 2)
#same result with lag fixed at usedlag of autolag
adf2 = tsast.adfuller(x, maxlag=2, autolag=None, regression=tr)
assert_almost_equal(adf3[:2], adf2[:2], decimal=12)
tr = 'c'
#check maxlag with autolag
adf3 = tsast.adfuller(x, maxlag=5, autolag='aic',
regression=tr, store=True, regresults=True)
assert_equal(len(adf3[-1].autolag_results), 5 + 1)
adf3 = tsast.adfuller(x, maxlag=0, autolag='aic',
regression=tr, store=True, regresults=True)
assert_equal(len(adf3[-1].autolag_results), 0 + 1)
def test_stata_writer_pandas():
buf = BytesIO()
dta = macrodata.load().data
dtype = dta.dtype
#as of 0.9.0 pandas only supports i8 and f8
dta = dta.astype(
np.dtype([('year', 'i8'), ('quarter', 'i8')] + dtype.descr[2:]))
dta4 = dta.astype(
np.dtype([('year', 'i4'), ('quarter', 'i4')] + dtype.descr[2:]))
dta = DataFrame.from_records(dta)
dta4 = DataFrame.from_records(dta4)
# dta is int64 'i8' given to Stata writer
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
dta5 = DataFrame.from_records(dta2)
# dta2 is int32 'i4' returned from Stata reader
if dta5.dtypes[1] is np.dtype('int64'):
ptesting.assert_frame_equal(dta.reset_index(), dta5)
else:
# don't check index because it has different size, int32 versus int64
ptesting.assert_frame_equal(dta4, dta5[dta5.columns[1:]])
def test_stata_writer_pandas():
buf = BytesIO()
dta = macrodata.load().data
dtype = dta.dtype
#as of 0.9.0 pandas only supports i8 and f8
dta = dta.astype(np.dtype([('year', 'i8'),
('quarter', 'i8')] + dtype.descr[2:]))
dta4 = dta.astype(np.dtype([('year', 'i4'),
('quarter', 'i4')] + dtype.descr[2:]))
dta = DataFrame.from_records(dta)
dta4 = DataFrame.from_records(dta4)
# dta is int64 'i8' given to Stata writer
writer = StataWriter(buf, dta)
writer.write_file()
buf.seek(0)
dta2 = genfromdta(buf)
dta5 = DataFrame.from_records(dta2)
# dta2 is int32 'i4' returned from Stata reader
if dta5.dtypes[1] is np.dtype('int64'):
ptesting.assert_frame_equal(dta.reset_index(), dta5)
else:
# don't check index because it has different size, int32 versus int64
ptesting.assert_frame_equal(dta4, dta5[dta5.columns[1:]])
from __future__ import print_function
import numpy as np
from statsmodels.regression.linear_model import OLS, GLSAR
from statsmodels.tools.tools import add_constant
from statsmodels.datasets import macrodata
import statsmodels.regression.tests.results.results_macro_ols_robust as res
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
res_olsg = OLS(g_inv, exogg).fit()
print(res_olsg.summary())
res_hc0 = res_olsg.get_robustcov_results('HC1')
print('\n\n')
print(res_hc0.summary())
print('\n\n')
res_hac4 = res_olsg.get_robustcov_results('HAC', maxlags=4, use_correction=True)
print(res_hac4.summary())
print('\n\n')
tt = res_hac4.t_test(np.eye(len(res_hac4.params)))
print(tt.summary())
print('\n\n')
print(tt.summary_frame())
def setup_class(cls):
cls.cpi = log(macrodata.load().data['cpi'])
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
def setup_class(cls):
cls.rng = RandomState(12345)
cls.cpi = log(load().data['cpi'])
cls.inflation = diff(cls.cpi)
def test_all(self):
d = macrodata.load().data
#import datasetswsm.greene as g
#d = g.load('5-1')
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
#simple diff, not growthrate, I want heteroscedasticity later for testing
endogd = np.diff(d['realinv'])
exogd = add_constant(np.c_[np.diff(d['realgdp']), d['realint'][:-1]])
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, d['realint'][:-1]])
res_ols = OLS(endogg, exogg).fit()
#print res_ols.params
mod_g1 = GLSAR(endogg, exogg, rho=-0.108136)
res_g1 = mod_g1.fit()
#print res_g1.params
mod_g2 = GLSAR(endogg, exogg, rho=-0.108136) #-0.1335859) from R
res_g2 = mod_g2.iterative_fit(maxiter=5)
#print res_g2.params
rho = -0.108136
# coefficient std. error t-ratio p-value 95% CONFIDENCE INTERVAL
partable = np.array([
[-9.50990, 0.990456, -9.602, 3.65e-018, -11.4631, -7.55670], # ***
[ 4.37040, 0.208146, 21.00, 2.93e-052, 3.95993, 4.78086], # ***
[-0.579253, 0.268009, -2.161, 0.0319, -1.10777, -0.0507346]]) # **
#Statistics based on the rho-differenced data:
result_gretl_g1 = dict(
endog_mean = ("Mean dependent var", 3.113973),
endog_std = ("S.D. dependent var", 18.67447),
ssr = ("Sum squared resid", 22530.90),
mse_resid_sqrt = ("S.E. of regression", 10.66735),
rsquared = ("R-squared", 0.676973),
rsquared_adj = ("Adjusted R-squared", 0.673710),
fvalue = ("F(2, 198)", 221.0475),
f_pvalue = ("P-value(F)", 3.56e-51),
resid_acf1 = ("rho", -0.003481),
dw = ("Durbin-Watson", 1.993858))
#fstatistic, p-value, df1, df2
reset_2_3 = [5.219019, 0.00619, 2, 197, "f"]
reset_2 = [7.268492, 0.00762, 1, 198, "f"]
reset_3 = [5.248951, 0.023, 1, 198, "f"]
#LM-statistic, p-value, df
arch_4 = [7.30776, 0.120491, 4, "chi2"]
#multicollinearity
vif = [1.002, 1.002]
cond_1norm = 6862.0664
determinant = 1.0296049e+009
reciprocal_condition_number = 0.013819244
#Chi-square(2): test-statistic, pvalue, df
normality = [20.2792, 3.94837e-005, 2]
#tests
res = res_g1 #with rho from Gretl
#basic
assert_almost_equal(res.params, partable[:,0], 4)
assert_almost_equal(res.bse, partable[:,1], 6)
assert_almost_equal(res.tvalues, partable[:,2], 2)
assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
#assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl
#assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=7) #FAIL
#assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=7) #FAIL
assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=4)
assert_approx_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], significant=2)
#assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO
#arch
#sm_arch = smsdia.acorr_lm(res.wresid**2, maxlag=4, autolag=None)
sm_arch = smsdia.het_arch(res.wresid, maxlag=4)
assert_almost_equal(sm_arch[0], arch_4[0], decimal=4)
assert_almost_equal(sm_arch[1], arch_4[1], decimal=6)
#tests
res = res_g2 #with estimated rho
#estimated lag coefficient
assert_almost_equal(res.model.rho, rho, decimal=3)
#basic
assert_almost_equal(res.params, partable[:,0], 4)
assert_almost_equal(res.bse, partable[:,1], 3)
assert_almost_equal(res.tvalues, partable[:,2], 2)
assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
#assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl
#assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=7) #FAIL
#assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=7) #FAIL
assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=0)
assert_almost_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], decimal=6)
#assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO
c = oi.reset_ramsey(res, degree=2)
compare_ftest(c, reset_2, decimal=(2,4))
c = oi.reset_ramsey(res, degree=3)
compare_ftest(c, reset_2_3, decimal=(2,4))
#arch
#sm_arch = smsdia.acorr_lm(res.wresid**2, maxlag=4, autolag=None)
sm_arch = smsdia.het_arch(res.wresid, maxlag=4)
assert_almost_equal(sm_arch[0], arch_4[0], decimal=1)
assert_almost_equal(sm_arch[1], arch_4[1], decimal=2)
'''
Performing iterative calculation of rho...
ITER RHO ESS
1 -0.10734 22530.9
2 -0.10814 22530.9
Model 4: Cochrane-Orcutt, using observations 1959:3-2009:3 (T = 201)
Dependent variable: ds_l_realinv
rho = -0.108136
coefficient std. error t-ratio p-value
-------------------------------------------------------------
const -9.50990 0.990456 -9.602 3.65e-018 ***
ds_l_realgdp 4.37040 0.208146 21.00 2.93e-052 ***
realint_1 -0.579253 0.268009 -2.161 0.0319 **
Statistics based on the rho-differenced data:
Mean dependent var 3.113973 S.D. dependent var 18.67447
Sum squared resid 22530.90 S.E. of regression 10.66735
R-squared 0.676973 Adjusted R-squared 0.673710
F(2, 198) 221.0475 P-value(F) 3.56e-51
rho -0.003481 Durbin-Watson 1.993858
'''
'''
RESET test for specification (squares and cubes)
Test statistic: F = 5.219019,
with p-value = P(F(2,197) > 5.21902) = 0.00619
RESET test for specification (squares only)
Test statistic: F = 7.268492,
with p-value = P(F(1,198) > 7.26849) = 0.00762
RESET test for specification (cubes only)
Test statistic: F = 5.248951,
with p-value = P(F(1,198) > 5.24895) = 0.023:
'''
'''
Test for ARCH of order 4
coefficient std. error t-ratio p-value
--------------------------------------------------------
alpha(0) 97.0386 20.3234 4.775 3.56e-06 ***
alpha(1) 0.176114 0.0714698 2.464 0.0146 **
alpha(2) -0.0488339 0.0724981 -0.6736 0.5014
alpha(3) -0.0705413 0.0737058 -0.9571 0.3397
alpha(4) 0.0384531 0.0725763 0.5298 0.5968
Null hypothesis: no ARCH effect is present
Test statistic: LM = 7.30776
with p-value = P(Chi-square(4) > 7.30776) = 0.120491:
'''
'''
Variance Inflation Factors
Minimum possible value = 1.0
Values > 10.0 may indicate a collinearity problem
ds_l_realgdp 1.002
realint_1 1.002
VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient
between variable j and the other independent variables
Properties of matrix X'X:
1-norm = 6862.0664
Determinant = 1.0296049e+009
Reciprocal condition number = 0.013819244
'''
'''
Test for ARCH of order 4 -
Null hypothesis: no ARCH effect is present
Test statistic: LM = 7.30776
with p-value = P(Chi-square(4) > 7.30776) = 0.120491
Test of common factor restriction -
Null hypothesis: restriction is acceptable
Test statistic: F(2, 195) = 0.426391
with p-value = P(F(2, 195) > 0.426391) = 0.653468
Test for normality of residual -
Null hypothesis: error is normally distributed
Test statistic: Chi-square(2) = 20.2792
with p-value = 3.94837e-005:
'''
#no idea what this is
'''
Augmented regression for common factor test
OLS, using observations 1959:3-2009:3 (T = 201)
Dependent variable: ds_l_realinv
coefficient std. error t-ratio p-value
---------------------------------------------------------------
const -10.9481 1.35807 -8.062 7.44e-014 ***
ds_l_realgdp 4.28893 0.229459 18.69 2.40e-045 ***
realint_1 -0.662644 0.334872 -1.979 0.0492 **
ds_l_realinv_1 -0.108892 0.0715042 -1.523 0.1294
ds_l_realgdp_1 0.660443 0.390372 1.692 0.0923 *
realint_2 0.0769695 0.341527 0.2254 0.8219
Sum of squared residuals = 22432.8
Test of common factor restriction
Test statistic: F(2, 195) = 0.426391, with p-value = 0.653468
'''
################ with OLS, HAC errors
#Model 5: OLS, using observations 1959:2-2009:3 (T = 202)
#Dependent variable: ds_l_realinv
#HAC standard errors, bandwidth 4 (Bartlett kernel)
#coefficient std. error t-ratio p-value 95% CONFIDENCE INTERVAL
#for confidence interval t(199, 0.025) = 1.972
partable = np.array([
[-9.48167, 1.17709, -8.055, 7.17e-014, -11.8029, -7.16049], # ***
[4.37422, 0.328787, 13.30, 2.62e-029, 3.72587, 5.02258], #***
[-0.613997, 0.293619, -2.091, 0.0378, -1.19300, -0.0349939]]) # **
result_gretl_g1 = dict(
endog_mean = ("Mean dependent var", 3.257395),
endog_std = ("S.D. dependent var", 18.73915),
ssr = ("Sum squared resid", 22799.68),
mse_resid_sqrt = ("S.E. of regression", 10.70380),
rsquared = ("R-squared", 0.676978),
rsquared_adj = ("Adjusted R-squared", 0.673731),
fvalue = ("F(2, 199)", 90.79971),
f_pvalue = ("P-value(F)", 9.53e-29),
llf = ("Log-likelihood", -763.9752),
aic = ("Akaike criterion", 1533.950),
bic = ("Schwarz criterion", 1543.875),
hqic = ("Hannan-Quinn", 1537.966),
resid_acf1 = ("rho", -0.107341),
dw = ("Durbin-Watson", 2.213805))
linear_logs = [1.68351, 0.430953, 2, "chi2"]
#for logs: dropping 70 nan or incomplete observations, T=133
#(res_ols.model.exog <=0).any(1).sum() = 69 ?not 70
linear_squares = [7.52477, 0.0232283, 2, "chi2"]
#Autocorrelation, Breusch-Godfrey test for autocorrelation up to order 4
lm_acorr4 = [1.17928, 0.321197, 4, 195, "F"]
lm2_acorr4 = [4.771043, 0.312, 4, "chi2"]
acorr_ljungbox4 = [5.23587, 0.264, 4, "chi2"]
#break
cusum_Harvey_Collier = [0.494432, 0.621549, 198, "t"] #stats.t.sf(0.494432, 198)*2
#see cusum results in files
break_qlr = [3.01985, 0.1, 3, 196, "maxF"] #TODO check this, max at 2001:4
break_chow = [13.1897, 0.00424384, 3, "chi2"] # break at 1984:1
arch_4 = [3.43473, 0.487871, 4, "chi2"]
normality = [23.962, 0.00001, 2, "chi2"]
het_white = [33.503723, 0.000003, 5, "chi2"]
het_breusch_pagan = [1.302014, 0.521520, 2, "chi2"] #TODO: not available
het_breusch_pagan_konker = [0.709924, 0.701200, 2, "chi2"]
reset_2_3 = [5.219019, 0.00619, 2, 197, "f"]
reset_2 = [7.268492, 0.00762, 1, 198, "f"]
reset_3 = [5.248951, 0.023, 1, 198, "f"] #not available
cond_1norm = 5984.0525
determinant = 7.1087467e+008
reciprocal_condition_number = 0.013826504
vif = [1.001, 1.001]
names = 'date residual leverage influence DFFITS'.split()
cur_dir = os.path.abspath(os.path.dirname(__file__))
fpath = os.path.join(cur_dir, 'results/leverage_influence_ols_nostars.txt')
lev = np.genfromtxt(fpath, skip_header=3, skip_footer=1,
converters={0:lambda s: s})
#either numpy 1.6 or python 3.2 changed behavior
if np.isnan(lev[-1]['f1']):
lev = np.genfromtxt(fpath, skip_header=3, skip_footer=2,
converters={0:lambda s: s})
lev.dtype.names = names
res = res_ols #for easier copying
cov_hac = sw.cov_hac_simple(res, nlags=4, use_correction=False)
bse_hac = sw.se_cov(cov_hac)
assert_almost_equal(res.params, partable[:,0], 5)
assert_almost_equal(bse_hac, partable[:,1], 5)
#TODO
assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=4) #not in gretl
assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=6) #FAIL
assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=6) #FAIL
assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
#f-value is based on cov_hac I guess
#res2 = res.get_robustcov_results(cov_type='HC1')
# TODO: fvalue differs from Gretl, trying any of the HCx
#assert_almost_equal(res2.fvalue, result_gretl_g1['fvalue'][1], decimal=0) #FAIL
#assert_approx_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], significant=1) #FAIL
#assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO
c = oi.reset_ramsey(res, degree=2)
compare_ftest(c, reset_2, decimal=(6,5))
c = oi.reset_ramsey(res, degree=3)
compare_ftest(c, reset_2_3, decimal=(6,5))
linear_sq = smsdia.linear_lm(res.resid, res.model.exog)
assert_almost_equal(linear_sq[0], linear_squares[0], decimal=6)
assert_almost_equal(linear_sq[1], linear_squares[1], decimal=7)
hbpk = smsdia.het_breuschpagan(res.resid, res.model.exog)
assert_almost_equal(hbpk[0], het_breusch_pagan_konker[0], decimal=6)
assert_almost_equal(hbpk[1], het_breusch_pagan_konker[1], decimal=6)
hw = smsdia.het_white(res.resid, res.model.exog)
assert_almost_equal(hw[:2], het_white[:2], 6)
#arch
#sm_arch = smsdia.acorr_lm(res.resid**2, maxlag=4, autolag=None)
sm_arch = smsdia.het_arch(res.resid, maxlag=4)
assert_almost_equal(sm_arch[0], arch_4[0], decimal=5)
assert_almost_equal(sm_arch[1], arch_4[1], decimal=6)
vif2 = [oi.variance_inflation_factor(res.model.exog, k) for k in [1,2]]
infl = oi.OLSInfluence(res_ols)
#print np.max(np.abs(lev['DFFITS'] - infl.dffits[0]))
#print np.max(np.abs(lev['leverage'] - infl.hat_matrix_diag))
#print np.max(np.abs(lev['influence'] - infl.influence)) #just added this based on Gretl
#just rough test, low decimal in Gretl output,
assert_almost_equal(lev['residual'], res.resid, decimal=3)
assert_almost_equal(lev['DFFITS'], infl.dffits[0], decimal=3)
assert_almost_equal(lev['leverage'], infl.hat_matrix_diag, decimal=3)
assert_almost_equal(lev['influence'], infl.influence, decimal=4)
def test_bking2d():
"""
Test Baxter-King band-pass filter with 2d input
"""
bking_results = array([[7.320813,-.0374475], [2.886914,-.0430094],
[-6.818976,-.053456], [-13.49436,-.0620739], [-13.27936,-.0626929],
[-9.405913,-.0603022], [-5.691091,-.0630016], [-5.133076,-.0832268],
[-7.273468,-.1186448], [-9.243364,-.1619868], [-8.482916,-.2116604],
[-4.447764,-.2670747], [2.406559,-.3209931], [10.68433,-.3583075],
[19.46414,-.3626742], [28.09749,-.3294618], [34.11066,-.2773388],
[33.48468,-.2436127], [24.64598,-.2605531], [9.952399,-.3305166],
[-4.265528,-.4275561], [-12.59471,-.5076068], [-13.46714,-.537573],
[-9.049501,-.5205845], [-3.011248,-.481673], [.5655082,-.4403994],
[2.897976,-.4039957], [7.406077,-.3537394], [14.67959,-.2687359],
[18.651,-.1459743], [13.05891,.0014926], [-2.945415,.1424277],
[-24.08659,.2451936], [-41.86147,.288541], [-48.68383,.2727282],
[-43.32689,.1959127], [-31.66654,.0644874], [-20.38356,-.1158372],
[-13.76411,-.3518627], [-9.978693,-.6557535], [-3.7704,-1.003754],
[10.27108,-1.341632], [31.02847,-1.614486], [51.87613,-1.779089],
[66.93117,-1.807459], [73.51951,-1.679688], [73.4053,-1.401012],
[69.17468,-.9954996], [59.8543,-.511261], [38.23899,-.0146745],
[-.2604809,.4261311], [-49.0107,.7452514], [-91.1128,.8879492],
[-112.1574,.8282748], [-108.3227,.5851508], [-86.51453,.2351699],
[-59.91258,-.1208998], [-40.01185,-.4297895], [-29.70265,-.6821963],
[-22.76396,-.9234254], [-13.08037,-1.217539], [1.913622,-1.57367],
[20.44045,-1.927008], [37.32873,-2.229565], [46.79802,-2.463154],
[51.95937,-2.614697], [59.67393,-2.681357], [70.50803,-2.609654],
[81.27311,-2.301618], [83.53191,-1.720974], [67.72536,-.9837123],
[33.78039,-.2261613], [-6.509092,.4546985], [-37.31579,1.005751],
[-46.05207,1.457224], [-29.81496,1.870815], [1.416417,2.263313],
[28.31503,2.599906], [32.90134,2.812282], [8.949259,2.83358],
[-35.41895,2.632667], [-84.65775,2.201077], [-124.4288,1.598951],
[-144.6036,.9504762], [-140.2204,.4187932], [-109.2624,.1646726],
[-53.6901,.2034265], [15.07415,.398165], [74.44268,.5427476],
[104.0403,.5454975], [101.0725,.4723354], [76.58291,.4626823],
[49.27925,.5840143], [36.15751,.7187981], [36.48799,.6058422],
[37.60897,.1221227], [27.75998,-.5891272], [4.216643,-1.249841],
[-23.20579,-1.594972], [-39.33292,-1.545968], [-36.6134,-1.275494],
[-20.90161,-1.035783], [-4.143123,-.9971732], [5.48432,-1.154264],
[9.270075,-1.29987], [13.69573,-1.240559], [22.16675,-.9662656],
[33.01987,-.6420301], [41.93186,-.4698712], [47.12222,-.4527797],
[48.62164,-.4407153], [47.30701,-.2416076], [40.20537,.2317583],
[22.37898,.8710276], [-7.133002,1.426177], [-43.3339,1.652785],
[-78.51229,1.488021], [-101.3684,1.072096], [-105.2179,.6496446],
[-90.97147,.4193682], [-68.30824,.41847], [-48.10113,.5253419],
[-35.60709,.595076], [-31.15775,.5509905], [-31.82346,.3755519],
[-32.49278,.1297979], [-28.22499,-.0916165], [-14.42852,-.2531037],
[10.1827,-.3220784], [36.64189,-.2660561], [49.43468,-.1358522],
[38.75517,-.0279508], [6.447761,.0168735], [-33.15883,.0315687],
[-62.60446,.0819507], [-72.87829,.2274033], [-66.54629,.4641401],
[-52.61205,.7211093], [-38.06676,.907773], [-26.19963,.9387103],
[-16.51492,.7940786], [-7.007577,.5026631], [.6125674,.1224996],
[7.866972,-.2714422], [14.8123,-.6273921], [22.52388,-.9124271],
[30.65265,-1.108861], [39.47801,-1.199206], [49.05027,-1.19908],
[59.02925,-1.139046], [72.88999,-.9775021], [95.08865,-.6592603],
[125.8983,-.1609712], [154.4283,.4796201], [160.7638,1.100565],
[130.6092,1.447148], [67.84406,1.359608], [-7.070272,.8931825],
[-68.08128,.2619787], [-99.39944,-.252208], [-104.911,-.4703874],
[-100.2372,-.4430657], [-98.11596,-.390683], [-104.2051,-.5647846],
[-114.0125,-.9397582], [-113.3475,-1.341633], [-92.98669,-1.567337],
[-51.91707,-1.504943], [-.7313812,-1.30576], [43.22938,-1.17151],
[64.62762,-1.136151], [64.07226,-1.050555], [59.35707,-.7308369],
[67.06026,-.1766731], [91.87247,.3898467], [124.4591,.8135461],
[151.2402,.9644226], [163.0648,.6865934], [154.6432,.0115685]])
X = macrodata.load().data[['realinv','cpi']].view((float,2))
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y,bking_results,4)
def setup_class(cls):
data = macrodata.load()
cls.x = data.data['realgdp']
cls.bc = BoxCox()
def test_cfitz_filter():
"""
Test Christiano-Fitzgerald Filter. Results taken from R.
"""
#NOTE: The Stata mata code and the matlab code it's based on are wrong.
cfilt_res = array([[0.712599537179426, 0.439563468233128],
[1.06824041304411, 0.352886666575907],
[1.19422467791128, 0.257297004260607],
[0.970845473140327, 0.114504692143872],
[0.467026976628563, -0.070734782329146],
[-0.089153511514031, -0.238609685132605],
[-0.452339254128573, -0.32376584042956],
[-0.513231214461187, -0.314288554228112],
[-0.352372578720063, -0.258815055101336],
[-0.160282602521333, -0.215076844089567],
[-0.0918782593827686, -0.194120745417214],
[-0.168083823205437, -0.158327420072693],
[-0.291595204965808, -0.0742727139742986],
[-0.348638756841307, 0.037008291163602],
[-0.304328040874631, 0.108196527328748],
[-0.215933150969686, 0.0869231107437175],
[-0.165632621390694, -0.0130556619786275],
[-0.182326839507151, -0.126570926191824],
[-0.223737786804725, -0.205535321806185],
[-0.228939291453403, -0.269110078201836],
[-0.185518327227038, -0.375976507132174],
[-0.143900152461529, -0.53760115656157],
[-0.162749541550174, -0.660065018626038],
[-0.236263634756884, -0.588542352053736],
[-0.275785854309211, -0.236867929421996],
[-0.173666515108109, 0.303436335579219],
[0.0963135720251639, 0.779772338801993],
[0.427070069032285, 0.929108075350647],
[0.629034743259998, 0.658330841002647],
[0.557941248993624, 0.118500049361018],
[0.227866624051603, -0.385048321099911],
[-0.179878859883227, -0.582223992561493],
[-0.428263000051965, -0.394053702908091],
[-0.381640684645912, 0.0445437406977307],
[-0.0942745548364887, 0.493997792757968],
[0.238132391504895, 0.764519811304315],
[0.431293754256291, 0.814755206427316],
[0.455010435813661, 0.745567043101108],
[0.452800768971269, 0.709401694610443],
[0.615754619329312, 0.798293251119636],
[1.00256335412457, 0.975856845059388],
[1.44841039351691, 1.09097252730799],
[1.64651971120370, 0.967823457118036],
[1.35534532901802, 0.522397724737059],
[0.580492790312048, -0.16941343361609],
[-0.410746188031773, -0.90760401289056],
[-1.26148406066881, -1.49592867122591],
[-1.75784179124566, -1.87404167409849],
[-1.94478553960064, -2.14586210891112],
[-2.03751202708559, -2.465855239868],
[-2.20376059354166, -2.86294187189049],
[-2.39722338315852, -3.15004697654831],
[-2.38032366161537, -3.01390466643222],
[-1.91798022532025, -2.23395210271226],
[-0.982318490353716, -0.861346053067472],
[0.199047030343412, 0.790266582335616],
[1.28582776574786, 2.33731327460104],
[2.03565905376430, 3.54085486821911],
[2.41201557412526, 4.36519456268955],
[2.52011070482927, 4.84810517685452],
[2.45618479815452, 4.92906708807477],
[2.22272146945388, 4.42591058990048],
[1.78307567169034, 3.20962906108388],
[1.18234431860844, 1.42568060336985],
[0.590069172333348, -0.461896808688991],
[0.19662302949837, -1.89020992539465],
[0.048307034171166, -2.53490571941987],
[-0.0141956981899000, -2.50020338531674],
[-0.230505187108187, -2.20625973569823],
[-0.700947410386801, -2.06643697511048],
[-1.27085123163060, -2.21536883679783],
[-1.64082547897928, -2.49016921117735],
[-1.62286182971254, -2.63948740221362],
[-1.31609762181362, -2.54685250637904],
[-1.03085567704873, -2.27157435428923],
[-1.01100120380112, -1.90404507430561],
[-1.19823958399826, -1.4123209792214],
[-1.26398933608383, -0.654000086153317],
[-0.904710628949692, 0.447960016248203],
[-0.151340093679588, 1.73970411237156],
[0.592926881165989, 2.85741581650685],
[0.851660587507523, 3.4410446351716],
[0.480324393352127, 3.36870271362297],
[-0.165153230782417, 2.82003806696544],
[-0.459235919375844, 2.12858991660866],
[0.0271158842479935, 1.55840980891556],
[1.18759188180671, 1.17980298478623],
[2.43238266962309, 0.904011534980672],
[3.08277213720132, 0.595286911949837],
[2.79953663720953, 0.148014782859571],
[1.73694442845833, -0.496297332023011],
[0.357638079951977, -1.33108149877570],
[-0.891418825216945, -2.22650083183366],
[-1.77646467793627, -2.89359299718574],
[-2.24614790863088, -2.97921619243347],
[-2.29048879096607, -2.30003092779280],
[-1.87929656465888, -1.05298381273274],
[-1.04510101454788, 0.215837488618531],
[0.00413338508394524, 0.937866257924888],
[0.906870625251025, 0.92664365343019],
[1.33869057593416, 0.518564571494679],
[1.22659678454440, 0.288096869652890],
[0.79380139656044, 0.541053084632774],
[0.38029431865832, 1.01905199983437],
[0.183929413600038, 1.10529586616777],
[0.140045425897033, 0.393618564826736],
[0.0337313182352219, -0.86431819007665],
[-0.269208622829813, -1.85638085246792],
[-0.687276639992166, -1.82275359004533],
[-1.00161592325614, -0.692695765071617],
[-1.06320089194036, 0.803577361347341],
[-0.927152307196776, 1.67366338751788],
[-0.786802101366614, 1.42564362251793],
[-0.772970884572502, 0.426446388877964],
[-0.81275662801789, -0.437721213831647],
[-0.686831250382476, -0.504255468075149],
[-0.237936463020255, 0.148656301898438],
[0.459631879129522, 0.832925905720478],
[1.12717379822508, 0.889455302576383],
[1.48640453200855, 0.268042676202216],
[1.46515245776211, -0.446505038539178],
[1.22993484959115, -0.563868578181134],
[1.0272100765927, 0.0996849952196907],
[0.979191212438404, 1.05053652824665],
[1.00733490030391, 1.51658415000556],
[0.932192535457706, 1.06262774912638],
[0.643374300839414, -0.0865180803476065],
[0.186885168954461, -1.24799408923277],
[-0.290842337365465, -1.80035611156538],
[-0.669446735516495, -1.58847333561510],
[-0.928915624595538, -0.932116966867929],
[-1.11758635926997, -0.307879396807850],
[-1.26832454569756, -0.00856199983957032],
[-1.35755577149251, -0.0303537516690989],
[-1.34244112665546, -0.196807620887435],
[-1.22227976023299, -0.342062643495923],
[-1.04601473486818, -0.390474392372016],
[-0.85158508717846, -0.322164402093596],
[-0.605033439160543, -0.126930141915954],
[-0.218304303942818, 0.179551077808122],
[0.352173017779006, 0.512327303000081],
[1.01389600097229, 0.733397490572755],
[1.55149778750607, 0.748740387440165],
[1.75499674757591, 0.601759717901009],
[1.56636057468633, 0.457705308377562],
[1.12239792537274, 0.470849913286519],
[0.655802600286141, 0.646142040378738],
[0.335285115340180, 0.824103600255079],
[0.173454596506888, 0.808068498175582],
[0.0666753011315252, 0.521488214487996],
[-0.0842367474816212, 0.0583493276173476],
[-0.285604762631464, -0.405958418332253],
[-0.465735422869919, -0.747800086512926],
[-0.563586691231348, -0.94982272350799],
[-0.598110322024572, -1.04736894794361],
[-0.65216025756061, -1.04858365218822],
[-0.789663117801624, -0.924145633093637],
[-0.984704045337959, -0.670740724179446],
[-1.12449565589348, -0.359476803003931],
[-1.07878318723543, -0.092290938944355],
[-0.775555435407062, 0.102132527529259],
[-0.231610677329856, 0.314409560305622],
[0.463192794235131, 0.663523546243286],
[1.17416973448423, 1.13156902460931],
[1.74112278814906, 1.48967153067024],
[2.00320855757084, 1.42571085941843],
[1.8529912317336, 0.802460519079555],
[1.30747261947211, -0.169219078629572],
[0.540237070403222, -1.01621539672694],
[-0.177136817092375, -1.3130784867977],
[-0.611981468823591, -0.982477824460773],
[-0.700240028737747, -0.344919609255406],
[-0.572396497740112, 0.125083535035390],
[-0.450934466600975, 0.142553112732280],
[-0.494020014254326, -0.211429053871656],
[-0.701707589094918, -0.599602868825992],
[-0.94721339346157, -0.710669870591623],
[-1.09297139748946, -0.47846194092245],
[-1.08850658866583, -0.082258450179988],
[-0.976082880696692, 0.235758921309309],
[-0.81885695346771, 0.365298185204303],
[-0.63165529525553, 0.384725179378064],
[-0.37983149226421, 0.460240196164378],
[-0.0375551354277652, 0.68580913832794],
[0.361996927427804, 0.984470835955107],
[0.739920615366072, 1.13195975020298],
[1.03583478061534, 0.88812510421667],
[1.25614938962160, 0.172561520611839],
[1.45295030231799, -0.804979390544485],
[1.64887158748426, -1.55662011197859],
[1.78022721495313, -1.52921975346218],
[1.71945683859668, -0.462240366424548],
[1.36728880239190, 1.31213774341268],
[0.740173894315912, 2.88362740582926],
[-0.0205364331835904, 3.20319080963167],
[-0.725643970956428, 1.75222466531151],
[-1.23900506689782, -0.998432917440275],
[-1.52651897508678, -3.72752870885448],
[-1.62857516631435, -5.00551707196292],
[-1.59657420180451, -4.18499132634584],
[-1.45489013276495, -1.81759097305637],
[-1.21309542313047, 0.722029457352468]])
dta = macrodata.load().data[['tbilrate', 'infl']].view((float, 2))[1:]
cyc, trend = cffilter(dta)
assert_almost_equal(cyc, cfilt_res, 8)
#do 1d
cyc, trend = cffilter(dta[:, 1])
assert_almost_equal(cyc, cfilt_res[:, 1], 8)
def test_all(self):
d = macrodata.load().data
#import datasetswsm.greene as g
#d = g.load('5-1')
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
#simple diff, not growthrate, I want heteroscedasticity later for testing
endogd = np.diff(d['realinv'])
exogd = add_constant(np.c_[np.diff(d['realgdp']), d['realint'][:-1]],
prepend=True)
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, d['realint'][:-1]],prepend=True)
res_ols = OLS(endogg, exogg).fit()
#print res_ols.params
mod_g1 = GLSAR(endogg, exogg, rho=-0.108136)
res_g1 = mod_g1.fit()
#print res_g1.params
mod_g2 = GLSAR(endogg, exogg, rho=-0.108136) #-0.1335859) from R
res_g2 = mod_g2.iterative_fit(maxiter=5)
#print res_g2.params
rho = -0.108136
# coefficient std. error t-ratio p-value 95% CONFIDENCE INTERVAL
partable = np.array([
[-9.50990, 0.990456, -9.602, 3.65e-018, -11.4631, -7.55670], # ***
[ 4.37040, 0.208146, 21.00, 2.93e-052, 3.95993, 4.78086], # ***
[-0.579253, 0.268009, -2.161, 0.0319, -1.10777, -0.0507346]]) # **
#Statistics based on the rho-differenced data:
result_gretl_g1 = dict(
endog_mean = ("Mean dependent var", 3.113973),
endog_std = ("S.D. dependent var", 18.67447),
ssr = ("Sum squared resid", 22530.90),
mse_resid_sqrt = ("S.E. of regression", 10.66735),
rsquared = ("R-squared", 0.676973),
rsquared_adj = ("Adjusted R-squared", 0.673710),
fvalue = ("F(2, 198)", 221.0475),
f_pvalue = ("P-value(F)", 3.56e-51),
resid_acf1 = ("rho", -0.003481),
dw = ("Durbin-Watson", 1.993858))
#fstatistic, p-value, df1, df2
reset_2_3 = [5.219019, 0.00619, 2, 197, "f"]
reset_2 = [7.268492, 0.00762, 1, 198, "f"]
reset_3 = [5.248951, 0.023, 1, 198, "f"]
#LM-statistic, p-value, df
arch_4 = [7.30776, 0.120491, 4, "chi2"]
#multicollinearity
vif = [1.002, 1.002]
cond_1norm = 6862.0664
determinant = 1.0296049e+009
reciprocal_condition_number = 0.013819244
#Chi-square(2): test-statistic, pvalue, df
normality = [20.2792, 3.94837e-005, 2]
#tests
res = res_g1 #with rho from Gretl
#basic
assert_almost_equal(res.params, partable[:,0], 4)
assert_almost_equal(res.bse, partable[:,1], 6)
assert_almost_equal(res.tvalues, partable[:,2], 2)
assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
#assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl
#assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=7) #FAIL
#assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=7) #FAIL
assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=4)
assert_approx_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], significant=2)
#assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO
#arch
#sm_arch = smsdia.acorr_lm(res.wresid**2, maxlag=4, autolag=None)
sm_arch = smsdia.het_arch(res.wresid, maxlag=4)
assert_almost_equal(sm_arch[0], arch_4[0], decimal=4)
assert_almost_equal(sm_arch[1], arch_4[1], decimal=6)
#tests
res = res_g2 #with estimated rho
#estimated lag coefficient
assert_almost_equal(res.model.rho, rho, decimal=3)
#basic
assert_almost_equal(res.params, partable[:,0], 4)
assert_almost_equal(res.bse, partable[:,1], 3)
assert_almost_equal(res.tvalues, partable[:,2], 2)
assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
#assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl
#assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=7) #FAIL
#assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=7) #FAIL
assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=0)
assert_almost_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], decimal=6)
#assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO
c = oi.reset_ramsey(res, degree=2)
compare_ftest(c, reset_2, decimal=(2,4))
c = oi.reset_ramsey(res, degree=3)
compare_ftest(c, reset_2_3, decimal=(2,4))
#arch
#sm_arch = smsdia.acorr_lm(res.wresid**2, maxlag=4, autolag=None)
sm_arch = smsdia.het_arch(res.wresid, maxlag=4)
assert_almost_equal(sm_arch[0], arch_4[0], decimal=1)
assert_almost_equal(sm_arch[1], arch_4[1], decimal=2)
'''
Performing iterative calculation of rho...
ITER RHO ESS
1 -0.10734 22530.9
2 -0.10814 22530.9
Model 4: Cochrane-Orcutt, using observations 1959:3-2009:3 (T = 201)
Dependent variable: ds_l_realinv
rho = -0.108136
coefficient std. error t-ratio p-value
-------------------------------------------------------------
const -9.50990 0.990456 -9.602 3.65e-018 ***
ds_l_realgdp 4.37040 0.208146 21.00 2.93e-052 ***
realint_1 -0.579253 0.268009 -2.161 0.0319 **
Statistics based on the rho-differenced data:
Mean dependent var 3.113973 S.D. dependent var 18.67447
Sum squared resid 22530.90 S.E. of regression 10.66735
R-squared 0.676973 Adjusted R-squared 0.673710
F(2, 198) 221.0475 P-value(F) 3.56e-51
rho -0.003481 Durbin-Watson 1.993858
'''
'''
RESET test for specification (squares and cubes)
Test statistic: F = 5.219019,
with p-value = P(F(2,197) > 5.21902) = 0.00619
RESET test for specification (squares only)
Test statistic: F = 7.268492,
with p-value = P(F(1,198) > 7.26849) = 0.00762
RESET test for specification (cubes only)
Test statistic: F = 5.248951,
with p-value = P(F(1,198) > 5.24895) = 0.023:
'''
'''
Test for ARCH of order 4
coefficient std. error t-ratio p-value
--------------------------------------------------------
alpha(0) 97.0386 20.3234 4.775 3.56e-06 ***
alpha(1) 0.176114 0.0714698 2.464 0.0146 **
alpha(2) -0.0488339 0.0724981 -0.6736 0.5014
alpha(3) -0.0705413 0.0737058 -0.9571 0.3397
alpha(4) 0.0384531 0.0725763 0.5298 0.5968
Null hypothesis: no ARCH effect is present
Test statistic: LM = 7.30776
with p-value = P(Chi-square(4) > 7.30776) = 0.120491:
'''
'''
Variance Inflation Factors
Minimum possible value = 1.0
Values > 10.0 may indicate a collinearity problem
ds_l_realgdp 1.002
realint_1 1.002
VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient
between variable j and the other independent variables
Properties of matrix X'X:
1-norm = 6862.0664
Determinant = 1.0296049e+009
Reciprocal condition number = 0.013819244
'''
'''
Test for ARCH of order 4 -
Null hypothesis: no ARCH effect is present
Test statistic: LM = 7.30776
with p-value = P(Chi-square(4) > 7.30776) = 0.120491
Test of common factor restriction -
Null hypothesis: restriction is acceptable
Test statistic: F(2, 195) = 0.426391
with p-value = P(F(2, 195) > 0.426391) = 0.653468
Test for normality of residual -
Null hypothesis: error is normally distributed
Test statistic: Chi-square(2) = 20.2792
with p-value = 3.94837e-005:
'''
#no idea what this is
'''
Augmented regression for common factor test
OLS, using observations 1959:3-2009:3 (T = 201)
Dependent variable: ds_l_realinv
coefficient std. error t-ratio p-value
---------------------------------------------------------------
const -10.9481 1.35807 -8.062 7.44e-014 ***
ds_l_realgdp 4.28893 0.229459 18.69 2.40e-045 ***
realint_1 -0.662644 0.334872 -1.979 0.0492 **
ds_l_realinv_1 -0.108892 0.0715042 -1.523 0.1294
ds_l_realgdp_1 0.660443 0.390372 1.692 0.0923 *
realint_2 0.0769695 0.341527 0.2254 0.8219
Sum of squared residuals = 22432.8
Test of common factor restriction
Test statistic: F(2, 195) = 0.426391, with p-value = 0.653468
'''
################ with OLS, HAC errors
#Model 5: OLS, using observations 1959:2-2009:3 (T = 202)
#Dependent variable: ds_l_realinv
#HAC standard errors, bandwidth 4 (Bartlett kernel)
#coefficient std. error t-ratio p-value 95% CONFIDENCE INTERVAL
#for confidence interval t(199, 0.025) = 1.972
partable = np.array([
[-9.48167, 1.17709, -8.055, 7.17e-014, -11.8029, -7.16049], # ***
[4.37422, 0.328787, 13.30, 2.62e-029, 3.72587, 5.02258], #***
[-0.613997, 0.293619, -2.091, 0.0378, -1.19300, -0.0349939]]) # **
result_gretl_g1 = dict(
endog_mean = ("Mean dependent var", 3.257395),
endog_std = ("S.D. dependent var", 18.73915),
ssr = ("Sum squared resid", 22799.68),
mse_resid_sqrt = ("S.E. of regression", 10.70380),
rsquared = ("R-squared", 0.676978),
rsquared_adj = ("Adjusted R-squared", 0.673731),
fvalue = ("F(2, 199)", 90.79971),
f_pvalue = ("P-value(F)", 9.53e-29),
llf = ("Log-likelihood", -763.9752),
aic = ("Akaike criterion", 1533.950),
bic = ("Schwarz criterion", 1543.875),
hqic = ("Hannan-Quinn", 1537.966),
resid_acf1 = ("rho", -0.107341),
dw = ("Durbin-Watson", 2.213805))
linear_logs = [1.68351, 0.430953, 2, "chi2"]
#for logs: dropping 70 nan or incomplete observations, T=133
#(res_ols.model.exog <=0).any(1).sum() = 69 ?not 70
linear_squares = [7.52477, 0.0232283, 2, "chi2"]
#Autocorrelation, Breusch-Godfrey test for autocorrelation up to order 4
lm_acorr4 = [1.17928, 0.321197, 4, 195, "F"]
lm2_acorr4 = [4.771043, 0.312, 4, "chi2"]
acorr_ljungbox4 = [5.23587, 0.264, 4, "chi2"]
#break
cusum_Harvey_Collier = [0.494432, 0.621549, 198, "t"] #stats.t.sf(0.494432, 198)*2
#see cusum results in files
break_qlr = [3.01985, 0.1, 3, 196, "maxF"] #TODO check this, max at 2001:4
break_chow = [13.1897, 0.00424384, 3, "chi2"] # break at 1984:1
arch_4 = [3.43473, 0.487871, 4, "chi2"]
normality = [23.962, 0.00001, 2, "chi2"]
het_white = [33.503723, 0.000003, 5, "chi2"]
het_breush_pagan = [1.302014, 0.521520, 2, "chi2"] #TODO: not available
het_breush_pagan_konker = [0.709924, 0.701200, 2, "chi2"]
reset_2_3 = [5.219019, 0.00619, 2, 197, "f"]
reset_2 = [7.268492, 0.00762, 1, 198, "f"]
reset_3 = [5.248951, 0.023, 1, 198, "f"] #not available
cond_1norm = 5984.0525
determinant = 7.1087467e+008
reciprocal_condition_number = 0.013826504
vif = [1.001, 1.001]
names = 'date residual leverage influence DFFITS'.split()
cur_dir = os.path.abspath(os.path.dirname(__file__))
fpath = os.path.join(cur_dir, 'results/leverage_influence_ols_nostars.txt')
lev = np.genfromtxt(fpath, skip_header=3, skip_footer=1,
converters={0:lambda s: s})
#either numpy 1.6 or python 3.2 changed behavior
if np.isnan(lev[-1]['f1']):
lev = np.genfromtxt(fpath, skip_header=3, skip_footer=2,
converters={0:lambda s: s})
lev.dtype.names = names
res = res_ols #for easier copying
cov_hac = sw.cov_hac_simple(res, nlags=4, use_correction=False)
bse_hac = sw.se_cov(cov_hac)
assert_almost_equal(res.params, partable[:,0], 5)
assert_almost_equal(bse_hac, partable[:,1], 5)
#TODO
assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
#assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl
assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=6) #FAIL
assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=6) #FAIL
assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
#f-value is based on cov_hac I guess
#assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=0) #FAIL
#assert_approx_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], significant=1) #FAIL
#assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO
c = oi.reset_ramsey(res, degree=2)
compare_ftest(c, reset_2, decimal=(6,5))
c = oi.reset_ramsey(res, degree=3)
compare_ftest(c, reset_2_3, decimal=(6,5))
linear_sq = smsdia.linear_lm(res.resid, res.model.exog)
assert_almost_equal(linear_sq[0], linear_squares[0], decimal=6)
assert_almost_equal(linear_sq[1], linear_squares[1], decimal=7)
hbpk = smsdia.het_breushpagan(res.resid, res.model.exog)
assert_almost_equal(hbpk[0], het_breush_pagan_konker[0], decimal=6)
assert_almost_equal(hbpk[1], het_breush_pagan_konker[1], decimal=6)
hw = smsdia.het_white(res.resid, res.model.exog)
assert_almost_equal(hw[:2], het_white[:2], 6)
#arch
#sm_arch = smsdia.acorr_lm(res.resid**2, maxlag=4, autolag=None)
sm_arch = smsdia.het_arch(res.resid, maxlag=4)
assert_almost_equal(sm_arch[0], arch_4[0], decimal=5)
assert_almost_equal(sm_arch[1], arch_4[1], decimal=6)
vif2 = [oi.variance_inflation_factor(res.model.exog, k) for k in [1,2]]
infl = oi.OLSInfluence(res_ols)
#print np.max(np.abs(lev['DFFITS'] - infl.dffits[0]))
#print np.max(np.abs(lev['leverage'] - infl.hat_matrix_diag))
#print np.max(np.abs(lev['influence'] - infl.influence)) #just added this based on Gretl
#just rough test, low decimal in Gretl output,
assert_almost_equal(lev['residual'], res.resid, decimal=3)
assert_almost_equal(lev['DFFITS'], infl.dffits[0], decimal=3)
assert_almost_equal(lev['leverage'], infl.hat_matrix_diag, decimal=3)
assert_almost_equal(lev['influence'], infl.influence, decimal=4)
def setup_class(cls):
from statsmodels.datasets.macrodata import load
cls.cpi = log(load().data['cpi'])
cls.inflation = diff(cls.cpi)
def notyet_atst(): # FIXME: make this a test or move/remove
d = macrodata.load(as_pandas=False).data
realinv = d['realinv']
realgdp = d['realgdp']
realint = d['realint']
endog = realinv
exog = add_constant(np.c_[realgdp, realint])
res_ols1 = OLS(endog, exog).fit()
#growth rates
gs_l_realinv = 400 * np.diff(np.log(d['realinv']))
gs_l_realgdp = 400 * np.diff(np.log(d['realgdp']))
lint = d['realint'][:-1]
tbilrate = d['tbilrate'][:-1]
endogg = gs_l_realinv
exogg = add_constant(np.c_[gs_l_realgdp, lint])
exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate])
res_ols = OLS(endogg, exogg).fit()
res_ols2 = OLS(endogg, exogg2).fit()
#the following were done accidentally with res_ols1 in R,
#with original Greene data
params = np.array(
[-272.3986041341653, 0.1779455206941112, 0.2149432424658157])
cov_hac_4 = np.array([
1321.569466333051, -0.2318836566017612, 37.01280466875694,
-0.2318836566017614, 4.602339488102263e-05, -0.0104687835998635,
37.012804668757, -0.0104687835998635, 21.16037144168061
]).reshape(3, 3, order='F')
cov_hac_10 = np.array([
2027.356101193361, -0.3507514463299015, 54.81079621448568,
-0.350751446329901, 6.953380432635583e-05, -0.01268990195095196,
54.81079621448564, -0.01268990195095195, 22.92512402151113
]).reshape(3, 3, order='F')
#goldfeld-quandt
het_gq_greater = dict(statistic=13.20512768685082,
df1=99,
df2=98,
pvalue=1.246141976112324e-30,
distr='f')
het_gq_less = dict(statistic=13.20512768685082, df1=99, df2=98, pvalue=1.)
het_gq_2sided = dict(statistic=13.20512768685082,
df1=99,
df2=98,
pvalue=1.246141976112324e-30,
distr='f')
#goldfeld-quandt, fraction = 0.5
het_gq_greater_2 = dict(statistic=87.1328934692124,
df1=48,
df2=47,
pvalue=2.154956842194898e-33,
distr='f')
gq = smsdia.het_goldfeldquandt(endog, exog, split=0.5)
compare_t_est(gq, het_gq_greater, decimal=(13, 14))
assert_equal(gq[-1], 'increasing')
harvey_collier = dict(stat=2.28042114041313,
df=199,
pvalue=0.02364236161988260,
distr='t')
#hc = harvtest(fm, order.by=ggdp , data = list())
harvey_collier_2 = dict(stat=0.7516918462158783,
df=199,
pvalue=0.4531244858006127,
distr='t')
from __future__ import print_function
import numpy as np
from statsmodels.regression.linear_model import OLS, GLSAR
from statsmodels.tools.tools import add_constant
from statsmodels.datasets import macrodata
import statsmodels.regression.tests.results.results_macro_ols_robust as res
d2 = macrodata.load(as_pandas=False).data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
res_olsg = OLS(g_inv, exogg).fit()
print(res_olsg.summary())
res_hc0 = res_olsg.get_robustcov_results('HC1')
print('\n\n')
print(res_hc0.summary())
print('\n\n')
res_hac4 = res_olsg.get_robustcov_results('HAC', maxlags=4, use_correction=True)
print(res_hac4.summary())
print('\n\n')
tt = res_hac4.t_test(np.eye(len(res_hac4.params)))
print(tt.summary())
print('\n\n')
print(tt.summary_frame())
def test_cfitz_filter():
"""
Test Christiano-Fitzgerald Filter. Results taken from R.
"""
#NOTE: The Stata mata code and the matlab code it's based on are wrong.
cfilt_res = array([[0.712599537179426,0.439563468233128],
[1.06824041304411,0.352886666575907],
[1.19422467791128,0.257297004260607],
[0.970845473140327,0.114504692143872],
[0.467026976628563,-0.070734782329146],
[-0.089153511514031,-0.238609685132605],
[-0.452339254128573,-0.32376584042956],
[-0.513231214461187,-0.314288554228112],
[-0.352372578720063,-0.258815055101336],
[-0.160282602521333,-0.215076844089567],
[-0.0918782593827686,-0.194120745417214],
[-0.168083823205437,-0.158327420072693],
[-0.291595204965808,-0.0742727139742986],
[-0.348638756841307,0.037008291163602],
[-0.304328040874631,0.108196527328748],
[-0.215933150969686,0.0869231107437175],
[-0.165632621390694,-0.0130556619786275],
[-0.182326839507151,-0.126570926191824],
[-0.223737786804725,-0.205535321806185],
[-0.228939291453403,-0.269110078201836],
[-0.185518327227038,-0.375976507132174],
[-0.143900152461529,-0.53760115656157],
[-0.162749541550174,-0.660065018626038],
[-0.236263634756884,-0.588542352053736],
[-0.275785854309211,-0.236867929421996],
[-0.173666515108109,0.303436335579219],
[0.0963135720251639,0.779772338801993],
[0.427070069032285,0.929108075350647],
[0.629034743259998,0.658330841002647],
[0.557941248993624,0.118500049361018],
[0.227866624051603,-0.385048321099911],
[-0.179878859883227,-0.582223992561493],
[-0.428263000051965,-0.394053702908091],
[-0.381640684645912,0.0445437406977307],
[-0.0942745548364887,0.493997792757968],
[0.238132391504895,0.764519811304315],
[0.431293754256291,0.814755206427316],
[0.455010435813661,0.745567043101108],
[0.452800768971269,0.709401694610443],
[0.615754619329312,0.798293251119636],
[1.00256335412457,0.975856845059388],
[1.44841039351691,1.09097252730799],
[1.64651971120370,0.967823457118036],
[1.35534532901802,0.522397724737059],
[0.580492790312048,-0.16941343361609],
[-0.410746188031773,-0.90760401289056],
[-1.26148406066881,-1.49592867122591],
[-1.75784179124566,-1.87404167409849],
[-1.94478553960064,-2.14586210891112],
[-2.03751202708559,-2.465855239868],
[-2.20376059354166,-2.86294187189049],
[-2.39722338315852,-3.15004697654831],
[-2.38032366161537,-3.01390466643222],
[-1.91798022532025,-2.23395210271226],
[-0.982318490353716,-0.861346053067472],
[0.199047030343412,0.790266582335616],
[1.28582776574786,2.33731327460104],
[2.03565905376430,3.54085486821911],
[2.41201557412526,4.36519456268955],
[2.52011070482927,4.84810517685452],
[2.45618479815452,4.92906708807477],
[2.22272146945388,4.42591058990048],
[1.78307567169034,3.20962906108388],
[1.18234431860844,1.42568060336985],
[0.590069172333348,-0.461896808688991],
[0.19662302949837,-1.89020992539465],
[0.048307034171166,-2.53490571941987],
[-0.0141956981899000,-2.50020338531674],
[-0.230505187108187,-2.20625973569823],
[-0.700947410386801,-2.06643697511048],
[-1.27085123163060,-2.21536883679783],
[-1.64082547897928,-2.49016921117735],
[-1.62286182971254,-2.63948740221362],
[-1.31609762181362,-2.54685250637904],
[-1.03085567704873,-2.27157435428923],
[-1.01100120380112,-1.90404507430561],
[-1.19823958399826,-1.4123209792214],
[-1.26398933608383,-0.654000086153317],
[-0.904710628949692,0.447960016248203],
[-0.151340093679588,1.73970411237156],
[0.592926881165989,2.85741581650685],
[0.851660587507523,3.4410446351716],
[0.480324393352127,3.36870271362297],
[-0.165153230782417,2.82003806696544],
[-0.459235919375844,2.12858991660866],
[0.0271158842479935,1.55840980891556],
[1.18759188180671,1.17980298478623],
[2.43238266962309,0.904011534980672],
[3.08277213720132,0.595286911949837],
[2.79953663720953,0.148014782859571],
[1.73694442845833,-0.496297332023011],
[0.357638079951977,-1.33108149877570],
[-0.891418825216945,-2.22650083183366],
[-1.77646467793627,-2.89359299718574],
[-2.24614790863088,-2.97921619243347],
[-2.29048879096607,-2.30003092779280],
[-1.87929656465888,-1.05298381273274],
[-1.04510101454788,0.215837488618531],
[0.00413338508394524,0.937866257924888],
[0.906870625251025,0.92664365343019],
[1.33869057593416,0.518564571494679],
[1.22659678454440,0.288096869652890],
[0.79380139656044,0.541053084632774],
[0.38029431865832,1.01905199983437],
[0.183929413600038,1.10529586616777],
[0.140045425897033,0.393618564826736],
[0.0337313182352219,-0.86431819007665],
[-0.269208622829813,-1.85638085246792],
[-0.687276639992166,-1.82275359004533],
[-1.00161592325614,-0.692695765071617],
[-1.06320089194036,0.803577361347341],
[-0.927152307196776,1.67366338751788],
[-0.786802101366614,1.42564362251793],
[-0.772970884572502,0.426446388877964],
[-0.81275662801789,-0.437721213831647],
[-0.686831250382476,-0.504255468075149],
[-0.237936463020255,0.148656301898438],
[0.459631879129522,0.832925905720478],
[1.12717379822508,0.889455302576383],
[1.48640453200855,0.268042676202216],
[1.46515245776211,-0.446505038539178],
[1.22993484959115,-0.563868578181134],
[1.0272100765927,0.0996849952196907],
[0.979191212438404,1.05053652824665],
[1.00733490030391,1.51658415000556],
[0.932192535457706,1.06262774912638],
[0.643374300839414,-0.0865180803476065],
[0.186885168954461,-1.24799408923277],
[-0.290842337365465,-1.80035611156538],
[-0.669446735516495,-1.58847333561510],
[-0.928915624595538,-0.932116966867929],
[-1.11758635926997,-0.307879396807850],
[-1.26832454569756,-0.00856199983957032],
[-1.35755577149251,-0.0303537516690989],
[-1.34244112665546,-0.196807620887435],
[-1.22227976023299,-0.342062643495923],
[-1.04601473486818,-0.390474392372016],
[-0.85158508717846,-0.322164402093596],
[-0.605033439160543,-0.126930141915954],
[-0.218304303942818,0.179551077808122],
[0.352173017779006,0.512327303000081],
[1.01389600097229,0.733397490572755],
[1.55149778750607,0.748740387440165],
[1.75499674757591,0.601759717901009],
[1.56636057468633,0.457705308377562],
[1.12239792537274,0.470849913286519],
[0.655802600286141,0.646142040378738],
[0.335285115340180,0.824103600255079],
[0.173454596506888,0.808068498175582],
[0.0666753011315252,0.521488214487996],
[-0.0842367474816212,0.0583493276173476],
[-0.285604762631464,-0.405958418332253],
[-0.465735422869919,-0.747800086512926],
[-0.563586691231348,-0.94982272350799],
[-0.598110322024572,-1.04736894794361],
[-0.65216025756061,-1.04858365218822],
[-0.789663117801624,-0.924145633093637],
[-0.984704045337959,-0.670740724179446],
[-1.12449565589348,-0.359476803003931],
[-1.07878318723543,-0.092290938944355],
[-0.775555435407062,0.102132527529259],
[-0.231610677329856,0.314409560305622],
[0.463192794235131,0.663523546243286],
[1.17416973448423,1.13156902460931],
[1.74112278814906,1.48967153067024],
[2.00320855757084,1.42571085941843],
[1.8529912317336,0.802460519079555],
[1.30747261947211,-0.169219078629572],
[0.540237070403222,-1.01621539672694],
[-0.177136817092375,-1.3130784867977],
[-0.611981468823591,-0.982477824460773],
[-0.700240028737747,-0.344919609255406],
[-0.572396497740112,0.125083535035390],
[-0.450934466600975,0.142553112732280],
[-0.494020014254326,-0.211429053871656],
[-0.701707589094918,-0.599602868825992],
[-0.94721339346157,-0.710669870591623],
[-1.09297139748946,-0.47846194092245],
[-1.08850658866583,-0.082258450179988],
[-0.976082880696692,0.235758921309309],
[-0.81885695346771,0.365298185204303],
[-0.63165529525553,0.384725179378064],
[-0.37983149226421,0.460240196164378],
[-0.0375551354277652,0.68580913832794],
[0.361996927427804,0.984470835955107],
[0.739920615366072,1.13195975020298],
[1.03583478061534,0.88812510421667],
[1.25614938962160,0.172561520611839],
[1.45295030231799,-0.804979390544485],
[1.64887158748426,-1.55662011197859],
[1.78022721495313,-1.52921975346218],
[1.71945683859668,-0.462240366424548],
[1.36728880239190,1.31213774341268],
[0.740173894315912,2.88362740582926],
[-0.0205364331835904,3.20319080963167],
[-0.725643970956428,1.75222466531151],
[-1.23900506689782,-0.998432917440275],
[-1.52651897508678,-3.72752870885448],
[-1.62857516631435,-5.00551707196292],
[-1.59657420180451,-4.18499132634584],
[-1.45489013276495,-1.81759097305637],
[-1.21309542313047,0.722029457352468]])
dta = macrodata.load().data[['tbilrate','infl']].view((float,2))[1:]
cyc, trend = cffilter(dta)
assert_almost_equal(cyc, cfilt_res, 8)
#do 1d
cyc, trend = cffilter(dta[:,1])
assert_almost_equal(cyc, cfilt_res[:,1], 8)
def setup_class(cls):
data = macrodata.load()
cls.x = data.data['realgdp']
cls.bc = BoxCox()
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 06 15:44:57 2013
Author: Josef Perktold
"""
import numpy as np
from numpy.testing import assert_almost_equal
from statsmodels.datasets import macrodata
import statsmodels.tsa.stattools as tsa_stats
# some example data
mdata = macrodata.load().data
mdata = mdata[['realgdp','realcons']]
data = mdata.view((float,2))
data = np.diff(np.log(data), axis=0)
#R: lmtest:grangertest
r_result = [0.243097, 0.7844328, 195, 2] #f_test
gr = tsa_stats.grangercausalitytests(data[:,1::-1], 2, verbose=False)
assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7)
assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'],
decimal=7)
lag = 2
print '\nTest Results for %d lags' % lag
print
print '\n'.join(['%-20s statistic: %f6.4 p-value: %f6.4' % (k, res[0], res[1])
def test_bking2d():
"""
Test Baxter-King band-pass filter with 2d input
"""
bking_results = array([[7.320813, -.0374475], [2.886914, -.0430094],
[-6.818976, -.053456], [-13.49436, -.0620739],
[-13.27936, -.0626929], [-9.405913, -.0603022],
[-5.691091, -.0630016], [-5.133076, -.0832268],
[-7.273468, -.1186448], [-9.243364, -.1619868],
[-8.482916, -.2116604], [-4.447764, -.2670747],
[2.406559, -.3209931], [10.68433, -.3583075],
[19.46414, -.3626742], [28.09749, -.3294618],
[34.11066, -.2773388], [33.48468, -.2436127],
[24.64598, -.2605531], [9.952399, -.3305166],
[-4.265528, -.4275561], [-12.59471, -.5076068],
[-13.46714, -.537573], [-9.049501, -.5205845],
[-3.011248, -.481673], [.5655082, -.4403994],
[2.897976, -.4039957], [7.406077, -.3537394],
[14.67959, -.2687359], [18.651, -.1459743],
[13.05891, .0014926], [-2.945415, .1424277],
[-24.08659, .2451936], [-41.86147, .288541],
[-48.68383, .2727282], [-43.32689, .1959127],
[-31.66654, .0644874], [-20.38356, -.1158372],
[-13.76411, -.3518627], [-9.978693, -.6557535],
[-3.7704, -1.003754], [10.27108, -1.341632],
[31.02847, -1.614486], [51.87613, -1.779089],
[66.93117, -1.807459], [73.51951, -1.679688],
[73.4053, -1.401012], [69.17468, -.9954996],
[59.8543, -.511261], [38.23899, -.0146745],
[-.2604809, .4261311], [-49.0107, .7452514],
[-91.1128, .8879492], [-112.1574, .8282748],
[-108.3227, .5851508], [-86.51453, .2351699],
[-59.91258, -.1208998], [-40.01185, -.4297895],
[-29.70265, -.6821963], [-22.76396, -.9234254],
[-13.08037, -1.217539], [1.913622, -1.57367],
[20.44045, -1.927008], [37.32873, -2.229565],
[46.79802, -2.463154], [51.95937, -2.614697],
[59.67393, -2.681357], [70.50803, -2.609654],
[81.27311, -2.301618], [83.53191, -1.720974],
[67.72536, -.9837123], [33.78039, -.2261613],
[-6.509092, .4546985], [-37.31579, 1.005751],
[-46.05207, 1.457224], [-29.81496, 1.870815],
[1.416417, 2.263313], [28.31503, 2.599906],
[32.90134, 2.812282], [8.949259, 2.83358],
[-35.41895, 2.632667], [-84.65775, 2.201077],
[-124.4288, 1.598951], [-144.6036, .9504762],
[-140.2204, .4187932], [-109.2624, .1646726],
[-53.6901, .2034265], [15.07415, .398165],
[74.44268, .5427476], [104.0403, .5454975],
[101.0725, .4723354], [76.58291, .4626823],
[49.27925, .5840143], [36.15751, .7187981],
[36.48799, .6058422], [37.60897, .1221227],
[27.75998, -.5891272], [4.216643, -1.249841],
[-23.20579, -1.594972], [-39.33292, -1.545968],
[-36.6134, -1.275494], [-20.90161, -1.035783],
[-4.143123, -.9971732], [5.48432, -1.154264],
[9.270075, -1.29987], [13.69573, -1.240559],
[22.16675, -.9662656], [33.01987, -.6420301],
[41.93186, -.4698712], [47.12222, -.4527797],
[48.62164, -.4407153], [47.30701, -.2416076],
[40.20537, .2317583], [22.37898, .8710276],
[-7.133002, 1.426177], [-43.3339, 1.652785],
[-78.51229, 1.488021], [-101.3684, 1.072096],
[-105.2179, .6496446], [-90.97147, .4193682],
[-68.30824, .41847], [-48.10113, .5253419],
[-35.60709, .595076], [-31.15775, .5509905],
[-31.82346, .3755519], [-32.49278, .1297979],
[-28.22499, -.0916165], [-14.42852, -.2531037],
[10.1827, -.3220784], [36.64189, -.2660561],
[49.43468, -.1358522], [38.75517, -.0279508],
[6.447761, .0168735], [-33.15883, .0315687],
[-62.60446, .0819507], [-72.87829, .2274033],
[-66.54629, .4641401], [-52.61205, .7211093],
[-38.06676, .907773], [-26.19963, .9387103],
[-16.51492, .7940786], [-7.007577, .5026631],
[.6125674, .1224996], [7.866972, -.2714422],
[14.8123, -.6273921], [22.52388, -.9124271],
[30.65265, -1.108861], [39.47801, -1.199206],
[49.05027, -1.19908], [59.02925, -1.139046],
[72.88999, -.9775021], [95.08865, -.6592603],
[125.8983, -.1609712], [154.4283, .4796201],
[160.7638, 1.100565], [130.6092, 1.447148],
[67.84406, 1.359608], [-7.070272, .8931825],
[-68.08128, .2619787], [-99.39944, -.252208],
[-104.911, -.4703874], [-100.2372, -.4430657],
[-98.11596, -.390683], [-104.2051, -.5647846],
[-114.0125, -.9397582], [-113.3475, -1.341633],
[-92.98669, -1.567337], [-51.91707, -1.504943],
[-.7313812, -1.30576], [43.22938, -1.17151],
[64.62762, -1.136151], [64.07226, -1.050555],
[59.35707, -.7308369], [67.06026, -.1766731],
[91.87247, .3898467], [124.4591, .8135461],
[151.2402, .9644226], [163.0648, .6865934],
[154.6432, .0115685]])
X = macrodata.load().data[['realinv', 'cpi']].view((float, 2))
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y, bking_results, 4)
import numpy as np
from statsmodels.datasets import macrodata
from statsmodels.regression.linear_model import OLS
from statsmodels.tools.tools import add_constant
d2 = macrodata.load().data
g_gdp = 400*np.diff(np.log(d2['realgdp']))
g_inv = 400*np.diff(np.log(d2['realinv']))
exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
res_olsg = OLS(g_inv, exogg).fit()
print(res_olsg.summary())
res_hc0 = res_olsg.get_robustcov_results('HC1')
print('\n\n')
print(res_hc0.summary())
print('\n\n')
res_hac4 = res_olsg.get_robustcov_results('HAC', maxlags=4, use_correction=True)
print(res_hac4.summary())
print('\n\n')
tt = res_hac4.t_test(np.eye(len(res_hac4.params)))
print(tt.summary())
print('\n\n')
print(tt.summary_frame())
res_hac4.use_t = False
def setup_class(cls):
cls.rng = RandomState(12345)
cls.cpi = log(macrodata.load().data['cpi'])
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
def test_hpfilter():
"""
Test Hodrick-Prescott Filter. Results taken from Stata.
"""
hpfilt_res = array([[3.951191484487844718e+01, 2.670837085155121713e+03],
[8.008853245681075350e+01, 2.698712467543189177e+03],
[4.887545512195401898e+01, 2.726612544878045810e+03],
[3.059193256079834100e+01, 2.754612067439201837e+03],
[6.488266733421960453e+01, 2.782816332665780465e+03],
[2.304024204546703913e+01, 2.811349757954532834e+03],
[-1.355312369487364776e+00, 2.840377312369487299e+03],
[-6.746236512580753697e+01, 2.870078365125807522e+03],
[-8.136743836853429457e+01, 2.900631438368534418e+03],
[-6.016789026443257171e+01, 2.932172890264432681e+03],
[-4.636922433138215638e+01, 2.964788224331382025e+03],
[-2.069533915570400495e+01, 2.998525339155703932e+03],
[-2.162152558595607843e+00, 3.033403152558595593e+03],
[-4.718647774311648391e+00, 3.069427647774311481e+03],
[-1.355645669169007306e+01, 3.106603456691690099e+03],
[-4.436926204475639679e+01, 3.144932262044756499e+03],
[-4.332027378211660107e+01, 3.184407273782116590e+03],
[-4.454697106352068658e+01, 3.224993971063520803e+03],
[-2.629875787765286077e+01, 3.266630757877652741e+03],
[-4.426119635629265758e+01, 3.309228196356292756e+03],
[-1.443441190762496262e+01, 3.352680411907625057e+03],
[-2.026686669186437939e+01, 3.396853866691864368e+03],
[-1.913700136208899494e+01, 3.441606001362089046e+03],
[-5.482458977940950717e+01, 3.486781589779409387e+03],
[-1.596244517937793717e+01, 3.532213445179378141e+03],
[-1.374011542874541192e+01, 3.577700115428745448e+03],
[1.325482813403914406e+01, 3.623030171865960710e+03],
[5.603040174253828809e+01, 3.667983598257461836e+03],
[1.030743373627105939e+02, 3.712348662637289181e+03],
[7.217534795943993231e+01, 3.755948652040559864e+03],
[5.462972503693208637e+01, 3.798671274963067845e+03],
[4.407065050666142270e+01, 3.840449349493338559e+03],
[3.749016270204992907e+01, 3.881249837297949853e+03],
[-1.511244199923112319e+00, 3.921067244199923152e+03],
[-9.093507374079763395e+00, 3.959919507374079785e+03],
[-1.685361946760258434e+01, 3.997823619467602384e+03],
[2.822211031434289907e+01, 4.034790889685657021e+03],
[6.117590627896424849e+01, 4.070822093721035344e+03],
[5.433135391434370831e+01, 4.105935646085656117e+03],
[3.810480376716623141e+01, 4.140188196232833434e+03],
[7.042964928802848590e+01, 4.173670350711971878e+03],
[4.996346842507591646e+01, 4.206496531574924120e+03],
[4.455282059571254649e+01, 4.238825179404287155e+03],
[-7.584961950576143863e+00, 4.270845961950576566e+03],
[-4.620339247697120300e+01, 4.302776392476971523e+03],
[-7.054024364552969928e+01, 4.334829243645529459e+03],
[-6.492941099801464588e+01, 4.367188410998014660e+03],
[-1.433567024239555394e+02, 4.399993702423955256e+03],
[-5.932834493089012540e+01, 4.433344344930889747e+03],
[-6.842096758743628016e+01, 4.467249967587436004e+03],
[-6.774011924654860195e+01, 4.501683119246548813e+03],
[-9.030958565658056614e+01, 4.536573585656580690e+03],
[-4.603981499136807543e+01, 4.571808814991368308e+03],
[2.588118806672991923e+01, 4.607219811933269739e+03],
[3.489419371912299539e+01, 4.642608806280876706e+03],
[7.675179642495095322e+01, 4.677794203575049323e+03],
[1.635497817724171910e+02, 4.712616218227582976e+03],
[1.856079654765617306e+02, 4.746963034523438182e+03],
[1.254269446392718237e+02, 4.780825055360728584e+03],
[1.387413113837174024e+02, 4.814308688616282780e+03],
[6.201826599282230745e+01, 4.847598734007177882e+03],
[4.122129542972197669e+01, 4.880966704570278125e+03],
[-4.120287475842360436e+01, 4.914722874758424041e+03],
[-9.486328233441963675e+01, 4.949203282334419782e+03],
[-1.894232132641573116e+02, 4.984718213264157384e+03],
[-1.895766639620087517e+02, 5.021518663962008759e+03],
[-1.464092413342650616e+02, 5.059737241334265491e+03],
[-1.218770668721217589e+02, 5.099388066872122181e+03],
[-4.973075629078175552e+01, 5.140393756290781312e+03],
[-5.365375213897277717e+01, 5.182600752138972894e+03],
[-7.175241524251214287e+01, 5.225824415242512259e+03],
[-7.834757283225462743e+01, 5.269846572832254424e+03],
[-6.264220687943907251e+01, 5.314404206879438789e+03],
[-3.054332122210325906e+00, 5.359185332122210639e+03],
[4.808218808024685131e+01, 5.403838811919753425e+03],
[2.781399326736391231e+00, 5.448011600673263274e+03],
[-2.197570415173231595e+01, 5.491380704151732061e+03],
[1.509441335012807031e+02, 5.533624866498719712e+03],
[1.658909029574851957e+02, 5.574409097042514986e+03],
[2.027292548049981633e+02, 5.613492745195001589e+03],
[1.752101578176061594e+02, 5.650738842182393455e+03],
[1.452808749847536092e+02, 5.686137125015246056e+03],
[1.535481629475025329e+02, 5.719786837052497503e+03],
[1.376169777998875361e+02, 5.751878022200112355e+03],
[1.257703080340770612e+02, 5.782696691965922582e+03],
[-2.524186846895645431e+01, 5.812614868468956047e+03],
[-6.546618027042404719e+01, 5.842083180270424236e+03],
[1.192352023580315290e+01, 5.871536479764196883e+03],
[1.043482970188742911e+02, 5.901368702981125352e+03],
[2.581376184768396342e+01, 5.931981238152316109e+03],
[6.634330880534071184e+01, 5.963840691194659485e+03],
[-4.236780162594641297e+01, 5.997429801625946311e+03],
[-1.759397735321817891e+02, 6.033272773532181418e+03],
[-1.827933311233055065e+02, 6.071867331123305121e+03],
[-2.472312362505917918e+02, 6.113601236250591683e+03],
[-2.877470049336488955e+02, 6.158748004933649099e+03],
[-2.634066336693540507e+02, 6.207426633669354487e+03],
[-1.819572770763625158e+02, 6.259576277076362203e+03],
[-1.175034606274621183e+02, 6.314971460627461965e+03],
[-4.769898649718379602e+01, 6.373272986497183410e+03],
[1.419578280287896632e+01, 6.434068217197121157e+03],
[6.267929662760798237e+01, 6.496914703372392069e+03],
[6.196413196753746888e+01, 6.561378868032462378e+03],
[5.019769125317907310e+01, 6.627066308746821051e+03],
[4.665364933213822951e+01, 6.693621350667861407e+03],
[3.662430749527266016e+01, 6.760719692504727391e+03],
[7.545680850246480986e+01, 6.828066191497535328e+03],
[6.052940492147536133e+01, 6.895388595078524304e+03],
[6.029518881462354329e+01, 6.962461811185376064e+03],
[2.187042136652689805e+01, 7.029098578633473153e+03],
[2.380067926824722235e+01, 7.095149320731752596e+03],
[-7.119129802169481991e+00, 7.160478129802169860e+03],
[-3.194497359120850888e+01, 7.224963973591208742e+03],
[-1.897137038934124575e+01, 7.288481370389341464e+03],
[-1.832687287845146784e+01, 7.350884872878451461e+03],
[4.600482336597542599e+01, 7.412017176634024509e+03],
[2.489047706403016491e+01, 7.471709522935970199e+03],
[6.305909392127250612e+01, 7.529821906078727807e+03],
[4.585212309498183458e+01, 7.586229876905018500e+03],
[9.314260180878318351e+01, 7.640848398191216802e+03],
[1.129819097095369216e+02, 7.693621090290463144e+03],
[1.204662123176703972e+02, 7.744549787682329224e+03],
[1.336860614601246198e+02, 7.793706938539875409e+03],
[1.034567175813735957e+02, 7.841240282418626521e+03],
[1.403118873372050075e+02, 7.887381112662795204e+03],
[1.271726169351004501e+02, 7.932425383064899506e+03],
[8.271925765282139764e+01, 7.976756742347178260e+03],
[-3.197432211752584408e+01, 8.020838322117525422e+03],
[-1.150209535194062482e+02, 8.065184953519406008e+03],
[-1.064694837456772802e+02, 8.110291483745677397e+03],
[-1.190428718925368230e+02, 8.156580871892536379e+03],
[-1.353635336292991269e+02, 8.204409533629299403e+03],
[-9.644348283027102298e+01, 8.254059482830271008e+03],
[-6.143413116116607853e+01, 8.305728131161165948e+03],
[-3.019161311097923317e+01, 8.359552613110980019e+03],
[1.384333163552582846e+00, 8.415631666836447039e+03],
[-4.156016073666614830e+01, 8.474045160736666730e+03],
[-4.843882841860977351e+01, 8.534873828418609264e+03],
[-6.706442838867042155e+01, 8.598172428388670596e+03],
[-2.019644488579979225e+01, 8.663965444885800025e+03],
[-4.316446881084630149e+00, 8.732235446881084499e+03],
[4.435061943264736328e+01, 8.802952380567352520e+03],
[2.820550564155564643e+01, 8.876083494358445023e+03],
[5.155624419490777655e+01, 8.951623755805092514e+03],
[-4.318760899315748247e+00, 9.029585760899315574e+03],
[-6.534632828542271454e+01, 9.110014328285422380e+03],
[-7.226757738268497633e+01, 9.192951577382684263e+03],
[-9.412378615444868046e+01, 9.278398786154448317e+03],
[-1.191240653288368776e+02, 9.366312065328836979e+03],
[-4.953669826751865912e+01, 9.456588698267518339e+03],
[-6.017251579067487910e+01, 9.549051515790675694e+03],
[-5.103438828313483100e+01, 9.643492388283135369e+03],
[-7.343057830678117170e+01, 9.739665578306781754e+03],
[-2.774245193054957781e+01, 9.837293451930549054e+03],
[-3.380481112519191811e+00, 9.936052481112519672e+03],
[-2.672779877794346248e+01, 1.003560179877794326e+04],
[-3.217342505148371856e+01, 1.013559842505148299e+04],
[-4.140567518359966925e+01, 1.023568267518359971e+04],
[-6.687756033938057953e+00, 1.033547475603393832e+04],
[7.300600408459467872e+01, 1.043456899591540605e+04],
[6.862345670680042531e+01, 1.053255554329319966e+04],
[5.497882461487461114e+01, 1.062907017538512628e+04],
[9.612244093055960548e+01, 1.072379155906944106e+04],
[1.978212770103891671e+02, 1.081643272298961165e+04],
[1.362772276848754700e+02, 1.090676677231512440e+04],
[2.637635494867263333e+02, 1.099469045051327339e+04],
[1.876813256815166824e+02, 1.108018567431848351e+04],
[1.711447873158413131e+02, 1.116339921268415856e+04],
[5.257586460826678376e+01, 1.124459513539173349e+04],
[4.710652228531762375e+01, 1.132414447771468258e+04],
[-6.237613484241046535e+01, 1.140245113484241119e+04],
[-9.982044354035315337e+01, 1.147994844354035376e+04],
[-7.916275548997509759e+01, 1.155703075548997549e+04],
[-9.526003459472303803e+01, 1.163403003459472347e+04],
[-1.147987680369169539e+02, 1.171122876803691724e+04],
[-1.900259054765901965e+02, 1.178884990547659072e+04],
[-2.212256473439556430e+02, 1.186704464734395515e+04],
[-2.071394278781845060e+02, 1.194584542787818464e+04],
[-8.968541528904825100e+01, 1.202514641528904758e+04],
[-6.189531564415665343e+01, 1.210471231564415575e+04],
[-5.662878162551714922e+01, 1.218425178162551674e+04],
[-4.961678134413705266e+01, 1.226343478134413635e+04],
[-3.836288992144181975e+01, 1.234189588992144127e+04],
[-8.956671991456460091e+00, 1.241923867199145570e+04],
[3.907028461866866564e+01, 1.249504271538133071e+04],
[1.865299000184495526e+01, 1.256888200999815490e+04],
[4.279803532226833340e+01, 1.264035496467773191e+04],
[3.962735362631610769e+01, 1.270907164637368442e+04],
[1.412691291877854383e+02, 1.277466887081221466e+04],
[1.256537791844366438e+02, 1.283680822081556289e+04],
[7.067642758858892194e+01, 1.289523957241141034e+04],
[1.108876647603192396e+02, 1.294979133523968085e+04],
[9.956490829291760747e+01, 1.300033609170708223e+04],
[1.571612709880937473e+02, 1.304681572901190702e+04],
[2.318746375812715996e+02, 1.308923436241872878e+04],
[2.635546670125277160e+02, 1.312769433298747208e+04],
[2.044220965739259555e+02, 1.316244290342607383e+04],
[2.213739418903714977e+02, 1.319389205810962812e+04],
[1.020184547767112235e+02, 1.322258154522328914e+04],
[-1.072694716663390864e+02, 1.324918947166633916e+04],
[-3.490477058718843182e+02, 1.327445770587188417e+04],
[-3.975570728533530200e+02, 1.329906107285335383e+04],
[-3.331152428080622485e+02, 1.332345624280806260e+04]])
dta = macrodata.load().data['realgdp']
res = column_stack((hpfilter(dta, 1600)))
assert_almost_equal(res, hpfilt_res, 6)
def test_hpfilter():
"""
Test Hodrick-Prescott Filter. Results taken from Stata.
"""
hpfilt_res = array([[3.951191484487844718e+01,2.670837085155121713e+03],
[8.008853245681075350e+01,2.698712467543189177e+03],
[4.887545512195401898e+01,2.726612544878045810e+03],
[3.059193256079834100e+01,2.754612067439201837e+03],
[6.488266733421960453e+01,2.782816332665780465e+03],
[2.304024204546703913e+01,2.811349757954532834e+03],
[-1.355312369487364776e+00,2.840377312369487299e+03],
[-6.746236512580753697e+01,2.870078365125807522e+03],
[-8.136743836853429457e+01,2.900631438368534418e+03],
[-6.016789026443257171e+01,2.932172890264432681e+03],
[-4.636922433138215638e+01,2.964788224331382025e+03],
[-2.069533915570400495e+01,2.998525339155703932e+03],
[-2.162152558595607843e+00,3.033403152558595593e+03],
[-4.718647774311648391e+00,3.069427647774311481e+03],
[-1.355645669169007306e+01,3.106603456691690099e+03],
[-4.436926204475639679e+01,3.144932262044756499e+03],
[-4.332027378211660107e+01,3.184407273782116590e+03],
[-4.454697106352068658e+01,3.224993971063520803e+03],
[-2.629875787765286077e+01,3.266630757877652741e+03],
[-4.426119635629265758e+01,3.309228196356292756e+03],
[-1.443441190762496262e+01,3.352680411907625057e+03],
[-2.026686669186437939e+01,3.396853866691864368e+03],
[-1.913700136208899494e+01,3.441606001362089046e+03],
[-5.482458977940950717e+01,3.486781589779409387e+03],
[-1.596244517937793717e+01,3.532213445179378141e+03],
[-1.374011542874541192e+01,3.577700115428745448e+03],
[1.325482813403914406e+01,3.623030171865960710e+03],
[5.603040174253828809e+01,3.667983598257461836e+03],
[1.030743373627105939e+02,3.712348662637289181e+03],
[7.217534795943993231e+01,3.755948652040559864e+03],
[5.462972503693208637e+01,3.798671274963067845e+03],
[4.407065050666142270e+01,3.840449349493338559e+03],
[3.749016270204992907e+01,3.881249837297949853e+03],
[-1.511244199923112319e+00,3.921067244199923152e+03],
[-9.093507374079763395e+00,3.959919507374079785e+03],
[-1.685361946760258434e+01,3.997823619467602384e+03],
[2.822211031434289907e+01,4.034790889685657021e+03],
[6.117590627896424849e+01,4.070822093721035344e+03],
[5.433135391434370831e+01,4.105935646085656117e+03],
[3.810480376716623141e+01,4.140188196232833434e+03],
[7.042964928802848590e+01,4.173670350711971878e+03],
[4.996346842507591646e+01,4.206496531574924120e+03],
[4.455282059571254649e+01,4.238825179404287155e+03],
[-7.584961950576143863e+00,4.270845961950576566e+03],
[-4.620339247697120300e+01,4.302776392476971523e+03],
[-7.054024364552969928e+01,4.334829243645529459e+03],
[-6.492941099801464588e+01,4.367188410998014660e+03],
[-1.433567024239555394e+02,4.399993702423955256e+03],
[-5.932834493089012540e+01,4.433344344930889747e+03],
[-6.842096758743628016e+01,4.467249967587436004e+03],
[-6.774011924654860195e+01,4.501683119246548813e+03],
[-9.030958565658056614e+01,4.536573585656580690e+03],
[-4.603981499136807543e+01,4.571808814991368308e+03],
[2.588118806672991923e+01,4.607219811933269739e+03],
[3.489419371912299539e+01,4.642608806280876706e+03],
[7.675179642495095322e+01,4.677794203575049323e+03],
[1.635497817724171910e+02,4.712616218227582976e+03],
[1.856079654765617306e+02,4.746963034523438182e+03],
[1.254269446392718237e+02,4.780825055360728584e+03],
[1.387413113837174024e+02,4.814308688616282780e+03],
[6.201826599282230745e+01,4.847598734007177882e+03],
[4.122129542972197669e+01,4.880966704570278125e+03],
[-4.120287475842360436e+01,4.914722874758424041e+03],
[-9.486328233441963675e+01,4.949203282334419782e+03],
[-1.894232132641573116e+02,4.984718213264157384e+03],
[-1.895766639620087517e+02,5.021518663962008759e+03],
[-1.464092413342650616e+02,5.059737241334265491e+03],
[-1.218770668721217589e+02,5.099388066872122181e+03],
[-4.973075629078175552e+01,5.140393756290781312e+03],
[-5.365375213897277717e+01,5.182600752138972894e+03],
[-7.175241524251214287e+01,5.225824415242512259e+03],
[-7.834757283225462743e+01,5.269846572832254424e+03],
[-6.264220687943907251e+01,5.314404206879438789e+03],
[-3.054332122210325906e+00,5.359185332122210639e+03],
[4.808218808024685131e+01,5.403838811919753425e+03],
[2.781399326736391231e+00,5.448011600673263274e+03],
[-2.197570415173231595e+01,5.491380704151732061e+03],
[1.509441335012807031e+02,5.533624866498719712e+03],
[1.658909029574851957e+02,5.574409097042514986e+03],
[2.027292548049981633e+02,5.613492745195001589e+03],
[1.752101578176061594e+02,5.650738842182393455e+03],
[1.452808749847536092e+02,5.686137125015246056e+03],
[1.535481629475025329e+02,5.719786837052497503e+03],
[1.376169777998875361e+02,5.751878022200112355e+03],
[1.257703080340770612e+02,5.782696691965922582e+03],
[-2.524186846895645431e+01,5.812614868468956047e+03],
[-6.546618027042404719e+01,5.842083180270424236e+03],
[1.192352023580315290e+01,5.871536479764196883e+03],
[1.043482970188742911e+02,5.901368702981125352e+03],
[2.581376184768396342e+01,5.931981238152316109e+03],
[6.634330880534071184e+01,5.963840691194659485e+03],
[-4.236780162594641297e+01,5.997429801625946311e+03],
[-1.759397735321817891e+02,6.033272773532181418e+03],
[-1.827933311233055065e+02,6.071867331123305121e+03],
[-2.472312362505917918e+02,6.113601236250591683e+03],
[-2.877470049336488955e+02,6.158748004933649099e+03],
[-2.634066336693540507e+02,6.207426633669354487e+03],
[-1.819572770763625158e+02,6.259576277076362203e+03],
[-1.175034606274621183e+02,6.314971460627461965e+03],
[-4.769898649718379602e+01,6.373272986497183410e+03],
[1.419578280287896632e+01,6.434068217197121157e+03],
[6.267929662760798237e+01,6.496914703372392069e+03],
[6.196413196753746888e+01,6.561378868032462378e+03],
[5.019769125317907310e+01,6.627066308746821051e+03],
[4.665364933213822951e+01,6.693621350667861407e+03],
[3.662430749527266016e+01,6.760719692504727391e+03],
[7.545680850246480986e+01,6.828066191497535328e+03],
[6.052940492147536133e+01,6.895388595078524304e+03],
[6.029518881462354329e+01,6.962461811185376064e+03],
[2.187042136652689805e+01,7.029098578633473153e+03],
[2.380067926824722235e+01,7.095149320731752596e+03],
[-7.119129802169481991e+00,7.160478129802169860e+03],
[-3.194497359120850888e+01,7.224963973591208742e+03],
[-1.897137038934124575e+01,7.288481370389341464e+03],
[-1.832687287845146784e+01,7.350884872878451461e+03],
[4.600482336597542599e+01,7.412017176634024509e+03],
[2.489047706403016491e+01,7.471709522935970199e+03],
[6.305909392127250612e+01,7.529821906078727807e+03],
[4.585212309498183458e+01,7.586229876905018500e+03],
[9.314260180878318351e+01,7.640848398191216802e+03],
[1.129819097095369216e+02,7.693621090290463144e+03],
[1.204662123176703972e+02,7.744549787682329224e+03],
[1.336860614601246198e+02,7.793706938539875409e+03],
[1.034567175813735957e+02,7.841240282418626521e+03],
[1.403118873372050075e+02,7.887381112662795204e+03],
[1.271726169351004501e+02,7.932425383064899506e+03],
[8.271925765282139764e+01,7.976756742347178260e+03],
[-3.197432211752584408e+01,8.020838322117525422e+03],
[-1.150209535194062482e+02,8.065184953519406008e+03],
[-1.064694837456772802e+02,8.110291483745677397e+03],
[-1.190428718925368230e+02,8.156580871892536379e+03],
[-1.353635336292991269e+02,8.204409533629299403e+03],
[-9.644348283027102298e+01,8.254059482830271008e+03],
[-6.143413116116607853e+01,8.305728131161165948e+03],
[-3.019161311097923317e+01,8.359552613110980019e+03],
[1.384333163552582846e+00,8.415631666836447039e+03],
[-4.156016073666614830e+01,8.474045160736666730e+03],
[-4.843882841860977351e+01,8.534873828418609264e+03],
[-6.706442838867042155e+01,8.598172428388670596e+03],
[-2.019644488579979225e+01,8.663965444885800025e+03],
[-4.316446881084630149e+00,8.732235446881084499e+03],
[4.435061943264736328e+01,8.802952380567352520e+03],
[2.820550564155564643e+01,8.876083494358445023e+03],
[5.155624419490777655e+01,8.951623755805092514e+03],
[-4.318760899315748247e+00,9.029585760899315574e+03],
[-6.534632828542271454e+01,9.110014328285422380e+03],
[-7.226757738268497633e+01,9.192951577382684263e+03],
[-9.412378615444868046e+01,9.278398786154448317e+03],
[-1.191240653288368776e+02,9.366312065328836979e+03],
[-4.953669826751865912e+01,9.456588698267518339e+03],
[-6.017251579067487910e+01,9.549051515790675694e+03],
[-5.103438828313483100e+01,9.643492388283135369e+03],
[-7.343057830678117170e+01,9.739665578306781754e+03],
[-2.774245193054957781e+01,9.837293451930549054e+03],
[-3.380481112519191811e+00,9.936052481112519672e+03],
[-2.672779877794346248e+01,1.003560179877794326e+04],
[-3.217342505148371856e+01,1.013559842505148299e+04],
[-4.140567518359966925e+01,1.023568267518359971e+04],
[-6.687756033938057953e+00,1.033547475603393832e+04],
[7.300600408459467872e+01,1.043456899591540605e+04],
[6.862345670680042531e+01,1.053255554329319966e+04],
[5.497882461487461114e+01,1.062907017538512628e+04],
[9.612244093055960548e+01,1.072379155906944106e+04],
[1.978212770103891671e+02,1.081643272298961165e+04],
[1.362772276848754700e+02,1.090676677231512440e+04],
[2.637635494867263333e+02,1.099469045051327339e+04],
[1.876813256815166824e+02,1.108018567431848351e+04],
[1.711447873158413131e+02,1.116339921268415856e+04],
[5.257586460826678376e+01,1.124459513539173349e+04],
[4.710652228531762375e+01,1.132414447771468258e+04],
[-6.237613484241046535e+01,1.140245113484241119e+04],
[-9.982044354035315337e+01,1.147994844354035376e+04],
[-7.916275548997509759e+01,1.155703075548997549e+04],
[-9.526003459472303803e+01,1.163403003459472347e+04],
[-1.147987680369169539e+02,1.171122876803691724e+04],
[-1.900259054765901965e+02,1.178884990547659072e+04],
[-2.212256473439556430e+02,1.186704464734395515e+04],
[-2.071394278781845060e+02,1.194584542787818464e+04],
[-8.968541528904825100e+01,1.202514641528904758e+04],
[-6.189531564415665343e+01,1.210471231564415575e+04],
[-5.662878162551714922e+01,1.218425178162551674e+04],
[-4.961678134413705266e+01,1.226343478134413635e+04],
[-3.836288992144181975e+01,1.234189588992144127e+04],
[-8.956671991456460091e+00,1.241923867199145570e+04],
[3.907028461866866564e+01,1.249504271538133071e+04],
[1.865299000184495526e+01,1.256888200999815490e+04],
[4.279803532226833340e+01,1.264035496467773191e+04],
[3.962735362631610769e+01,1.270907164637368442e+04],
[1.412691291877854383e+02,1.277466887081221466e+04],
[1.256537791844366438e+02,1.283680822081556289e+04],
[7.067642758858892194e+01,1.289523957241141034e+04],
[1.108876647603192396e+02,1.294979133523968085e+04],
[9.956490829291760747e+01,1.300033609170708223e+04],
[1.571612709880937473e+02,1.304681572901190702e+04],
[2.318746375812715996e+02,1.308923436241872878e+04],
[2.635546670125277160e+02,1.312769433298747208e+04],
[2.044220965739259555e+02,1.316244290342607383e+04],
[2.213739418903714977e+02,1.319389205810962812e+04],
[1.020184547767112235e+02,1.322258154522328914e+04],
[-1.072694716663390864e+02,1.324918947166633916e+04],
[-3.490477058718843182e+02,1.327445770587188417e+04],
[-3.975570728533530200e+02,1.329906107285335383e+04],
[-3.331152428080622485e+02,1.332345624280806260e+04]])
dta = macrodata.load().data['realgdp']
res = column_stack((hpfilter(dta,1600)))
assert_almost_equal(res,hpfilt_res,6)
def setup_class(cls):
cls.cpi = log(macrodata.load().data['cpi'])
cls.inflation = diff(cls.cpi)
cls.inflation_change = diff(cls.inflation)
class SetupKPSS(object):
data = macrodata.load()
x = data.data['realgdp']
cv_50 = mackinnoncrit(nobs=50)
cv_inf = mackinnoncrit()
assert np.all(cv_50 <= cv_inf)
def test_adf_short_timeseries():
# GH 262
import numpy as np
from arch.unitroot import ADF
x = np.asarray([0., 0., 0., 0., 0., 0., 1., 1., 0., 0.])
adf = ADF(x)
assert_almost_equal(adf.stat, -2.236, decimal=3)
assert adf.lags == 1
kpss_autolag_data = ((macrodata.load().data['realgdp'], 'c', 9),
(sunspots.load().data['SUNACTIVITY'], 'c', 7),
(nile.load().data['volume'], 'c', 5),
(randhie.load().data['lncoins'], 'ct', 75),
(modechoice.load().data['invt'], 'ct', 18))
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
@pytest.mark.parametrize('data,trend,lags', kpss_autolag_data)
def test_kpss_data_dependent_lags(data, trend, lags):
# real GDP from macrodata data set
kpss = KPSS(data, trend=trend)
assert_equal(kpss.lags, lags)
za_test_result = namedtuple('za_test_result',
