order = 3
exog = x**np.arange(order + 1)
beta = np.array([1, 1, 0.1, 0.0])[:order+1] # 1. / np.arange(1, order + 2)
y_true = np.dot(exog, beta)
y = y_true + sig_e * np.random.normal(size=nobs)
endog = y
print('DGP')
print('nobs=%d, beta=%r, sig_e=%3.1f' % (nobs, beta, sig_e))
mod_ols = OLS(endog, exog[:,:2])
res_ols = mod_ols.fit()
#'cv_ls'[1000, 0.5][0.01, 0.45]
tst = smke.TestFForm(endog, exog[:,:2], bw=[0.01, 0.45], var_type='cc',
fform=lambda x,p: mod_ols.predict(p,x),
estimator=lambda y,x: OLS(y,x).fit().params,
nboot=1000)
print('bw', tst.bw)
print('tst.test_stat', tst.test_stat)
print(tst.sig)
print('tst.boots_results mean, min, max', (tst.boots_results.mean(),
tst.boots_results.min(),
tst.boots_results.max()))
print('lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean())
print('upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean())
from scipy import stats
print('aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2)
print('aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat))
do_plot=True
order = 2
exog = x**np.arange(1, order + 1)
beta = np.array([2,
-0.2])[:order + 1 - 1] # 1. / np.arange(1, order + 2)
y_true = np.dot(exog, beta)
y = y_true + sig_e * np.random.normal(size=nobs)
endog = y
mod_ols = OLS(endog, exog[:, :1])
#res_ols = mod_ols.fit()
#'cv_ls'[1000, 0.5]
bw_lw = [1. / np.sqrt(12.) * nobs**(-0.2)] * 2 #(-1. / 5.)
tst = smke.TestFForm(endog,
exog[:, :1],
bw=bw_lw,
var_type='c',
fform=lambda x, p: mod_ols.predict(p, x),
estimator=lambda y, x: OLS(y, x).fit().params,
nboot=399)
b_res.append([
tst.test_stat,
stats.norm.sf(tst.test_stat),
(tst.boots_results > tst.test_stat).mean()
])
t1 = time.time()
b_res = np.asarray(b_res)
print('time', (t1 - t0) / 60.)
print(b_res.mean(0))
print(b_res.std(0))
print('reject at [0.2, 0.1, 0.05] (row 1: normal, row 2: bootstrap)')