def gen_two_X(self):
'''
Given the info supplied in variable `order`, generate two separate
X data arrays for the two separate regimes.
Careful: no constants are being added here yet.
'''
k_upper = self.k_upper
k_lower = self.k_lower
max_upper = self.lag_upper
max_lower = self.lag_lower
max_both = self.lag_both
max_lag = self.max_lag
XX = self._X
upper_clag = k_upper[0]
X_upper = add_lag(XX[:,0],lags=upper_clag)[(max_lag-upper_clag):,1:]
lower_clag = k_lower[0]
X_lower = add_lag(XX[:,0],lags=lower_clag)[(max_lag-lower_clag):,1:]
for i1,lago in enumerate(k_upper[1:]):
if lago > upper_clag: upper_clag = lago
X_upper = np.hstack((X_upper,
add_lag(XX[(max_lag-upper_clag):,i1+1],lags=upper_clag)[:,1:]))
upper_clag = lago
for i1,lago in enumerate(k_lower[1:]):
if lago > lower_clag: lower_clag = lago
X_lower = np.hstack((X_lower,
add_lag(XX[(max_lag-lower_clag):,i1+1],lags=lower_clag)[:,1:]))
self.X_upper = X_upper
self.X_lower = X_lower
def test_add_lag1d_drop(self):
data = self.random_data
lagmat = stattools.lagmat(data, 3, trim="Both")
lag_data = tools.add_lag(data, lags=3, drop=True, insert=True)
assert_equal(lagmat, lag_data)
# no insert, should be the same
lag_data = tools.add_lag(data, lags=3, drop=True, insert=False)
assert_equal(lagmat, lag_data)
def test_add_lag_noinsertatend_ndarray(self):
data = self.macro_df.values
nddata = data.astype(float)
lagmat = stattools.lagmat(nddata[:, -1], 3, trim="Both")
results = np.column_stack((nddata[3:, :], lagmat))
lag_data = tools.add_lag(nddata, 3, 3, insert=False)
assert_equal(lag_data, results)
# should be the same as insert also check negative col number
lag_data2 = tools.add_lag(nddata, -1, 3, insert=True)
assert_equal(lag_data2, results)
def test_add_lag_noinsert_atend(self):
data = self.macro_df.values
nddata = data.astype(float)
lagmat = stattools.lagmat(nddata[:, -1], 3, trim="Both")
results = np.column_stack((nddata[3:, :], lagmat))
lag_data = tools.add_lag(data, self.cpi_loc, 3, insert=False)
assert_equal(lag_data, results)
# should be the same as insert
lag_data2 = tools.add_lag(data, self.cpi_loc, 3, insert=True)
assert_equal(lag_data2, results)
def test_add_lag1d(self):
data = self.random_data
lagmat = stattools.lagmat(data, 3, trim="Both")
results = np.column_stack((data[3:], lagmat))
lag_data = tools.add_lag(data, lags=3, insert=True)
assert_equal(results, lag_data)
# add index
data = data[:, None]
lagmat = stattools.lagmat(data, 3, trim="Both") # test for lagmat too
results = np.column_stack((data[3:], lagmat))
lag_data = tools.add_lag(data, lags=3, insert=True)
assert_equal(results, lag_data)
def test_add_lag1d_struct(self):
data = np.zeros(100, dtype=[("variable", float)])
nddata = self.random_data
data["variable"] = nddata
lagmat = stattools.lagmat(nddata, 3, trim="Both", original="in")
lag_data = tools.add_lag(data, 0, lags=3, insert=True)
assert_equal(lagmat, lag_data)
lag_data = tools.add_lag(data, 0, lags=3, insert=False)
assert_equal(lagmat, lag_data)
lag_data = tools.add_lag(data, lags=3, insert=True)
assert_equal(lagmat, lag_data)
def test_add_lag_drop_insert(self):
data = self.macro_df.values
nddata = data.astype(float)
lagmat = stattools.lagmat(nddata[:, 2], 3, trim="Both")
results = np.column_stack((nddata[3:, :2], lagmat, nddata[3:, -1]))
lag_data = tools.add_lag(data, self.realgdp_loc, 3, drop=True)
assert_equal(lag_data, results)
def test_add_lag_noinsert_ndarray(self):
data = self.macro_df.values
nddata = data.astype(float)
lagmat = stattools.lagmat(nddata[:, 2], 3, trim="Both")
results = np.column_stack((nddata[3:, :], lagmat))
lag_data = tools.add_lag(nddata, 2, 3, insert=False)
assert_equal(lag_data, results)
def test_add_lag_1d_drop_struct(self):
data = np.zeros(100, dtype=[("variable", float)])
nddata = self.random_data
data["variable"] = nddata
lagmat = stattools.lagmat(nddata, 3, trim="Both")
lag_data = tools.add_lag(data, lags=3, drop=True)
assert_equal(lagmat, lag_data)
