99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
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"""Tests for sandwich robust covariance estimation
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see also in regression for cov_hac compared to Gretl and
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sandbox.panel test_random_panel for comparing cov_cluster, cov_hac_panel and
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cov_white
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Created on Sat Dec 17 08:39:16 2011
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Author: Josef Perktold
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"""
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_allclose
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from statsmodels.regression.linear_model import OLS
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from statsmodels.tools.tools import add_constant
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import statsmodels.stats.sandwich_covariance as sw
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def test_cov_cluster_2groups():
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# comparing cluster robust standard errors to Peterson
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# requires Petersen's test_data
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# http://www.kellogg.northwestern.edu/faculty/petersen
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# .../htm/papers/se/test_data.txt
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import os
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cur_dir = os.path.abspath(os.path.dirname(__file__))
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fpath = os.path.join(cur_dir, "test_data.txt")
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pet = np.genfromtxt(fpath)
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endog = pet[:, -1]
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group = pet[:, 0].astype(int)
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time = pet[:, 1].astype(int)
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exog = add_constant(pet[:, 2])
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res = OLS(endog, exog).fit()
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cov01, covg, covt = sw.cov_cluster_2groups(res, group, group2=time)
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# Reference number from Petersen
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# http://www.kellogg.northwestern.edu/faculty/petersen/htm
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# .../papers/se/test_data.htm
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bse_petw = [0.0284, 0.0284]
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bse_pet0 = [0.0670, 0.0506]
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bse_pet1 = [0.0234, 0.0334] # year
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bse_pet01 = [0.0651, 0.0536] # firm and year
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bse_0 = sw.se_cov(covg)
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bse_1 = sw.se_cov(covt)
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bse_01 = sw.se_cov(cov01)
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# print res.HC0_se, bse_petw - res.HC0_se
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# print bse_0, bse_0 - bse_pet0
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# print bse_1, bse_1 - bse_pet1
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# print bse_01, bse_01 - bse_pet01
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assert_almost_equal(bse_petw, res.HC0_se, decimal=4)
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assert_almost_equal(bse_0, bse_pet0, decimal=4)
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assert_almost_equal(bse_1, bse_pet1, decimal=4)
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assert_almost_equal(bse_01, bse_pet01, decimal=4)
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def test_hac_simple():
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from statsmodels.datasets import macrodata
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d2 = macrodata.load_pandas().data
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g_gdp = 400 * np.diff(np.log(d2['realgdp'].values))
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g_inv = 400 * np.diff(np.log(d2['realinv'].values))
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exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1].values])
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res_olsg = OLS(g_inv, exogg).fit()
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# > NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE,
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# verbose=TRUE, adjust=TRUE)
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# Lag truncation parameter chosen: 4
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# (Intercept) ggdp lint
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cov1_r = [
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[+1.40643899878678802, -0.3180328707083329709, -0.060621111216488610],
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[-0.31803287070833292, 0.1097308348999818661, +0.000395311760301478],
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[-0.06062111121648865, 0.0003953117603014895, +0.087511528912470993]
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]
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# > NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE,
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# verbose=TRUE, adjust=FALSE)
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# Lag truncation parameter chosen: 4
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# (Intercept) ggdp lint
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cov2_r = [
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[+1.3855512908840137, -0.313309610252268500, -0.059720797683570477],
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[-0.3133096102522685, +0.108101169035130618, +0.000389440793564339],
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[-0.0597207976835705, +0.000389440793564336, +0.086211852740503622]
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]
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cov1 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=True)
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se1 = sw.se_cov(cov1)
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cov2 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False)
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se2 = sw.se_cov(cov2)
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# Relax precision requirements for this test due to failure in NumPy 1.23
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assert_allclose(cov1, cov1_r)
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assert_allclose(cov2, cov2_r)
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assert_allclose(np.sqrt(np.diag(cov1_r)), se1)
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assert_allclose(np.sqrt(np.diag(cov2_r)), se2)
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# compare default for nlags
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cov3 = sw.cov_hac_simple(res_olsg, use_correction=False)
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cov4 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False)
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assert_allclose(cov3, cov4)
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