294 lines
12 KiB
Python
294 lines
12 KiB
Python
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# STATA adds a constant no matter if you want to or not,
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# so I cannot test for having no intercept. This also would make
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# no sense for Oaxaca. All of these stata_results
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# are from using the oaxaca command in STATA.
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# Variance from STATA is bootstrapped. Sometimes STATA
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# does not converge correctly, so mulitple iterations
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# must be done.
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import numpy as np
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from statsmodels.datasets.ccard.data import load_pandas
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from statsmodels.stats.oaxaca import OaxacaBlinder
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from statsmodels.tools.tools import add_constant
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pandas_df = load_pandas()
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endog = pandas_df.endog.values
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exog = add_constant(pandas_df.exog.values, prepend=False)
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pd_endog, pd_exog = pandas_df.endog, add_constant(
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pandas_df.exog, prepend=False
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)
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class TestOaxaca:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(endog, exog, 3)
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def test_results(self):
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np.random.seed(0)
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stata_results = np.array([158.7504, 321.7482, 75.45371, -238.4515])
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stata_results_pooled = np.array([158.7504, 130.8095, 27.94091])
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stata_results_std = np.array([653.10389, 64.584796, 655.0323717])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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endow_var, coef_var, inter_var = self.model.three_fold(True).std
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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np.testing.assert_almost_equal(endow_var, stata_results_std[0], 3)
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np.testing.assert_almost_equal(coef_var, stata_results_std[1], 3)
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np.testing.assert_almost_equal(inter_var, stata_results_std[2], 3)
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class TestOaxacaNoSwap:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(endog, exog, 3, swap=False)
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def test_results(self):
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stata_results = np.array([-158.7504, -83.29674, 162.9978, -238.4515])
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stata_results_pooled = np.array([-158.7504, -130.8095, -27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOaxacaPandas:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(pd_endog, pd_exog, "OWNRENT")
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def test_results(self):
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stata_results = np.array([158.7504, 321.7482, 75.45371, -238.4515])
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stata_results_pooled = np.array([158.7504, 130.8095, 27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOaxacaPandasNoSwap:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(pd_endog, pd_exog, "OWNRENT", swap=False)
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def test_results(self):
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stata_results = np.array([-158.7504, -83.29674, 162.9978, -238.4515])
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stata_results_pooled = np.array([-158.7504, -130.8095, -27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOaxacaNoConstPassed:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(
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pandas_df.endog.values, pandas_df.exog.values, 3, hasconst=False
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)
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def test_results(self):
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stata_results = np.array([158.7504, 321.7482, 75.45371, -238.4515])
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stata_results_pooled = np.array([158.7504, 130.8095, 27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOaxacaNoSwapNoConstPassed:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(
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pandas_df.endog.values,
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pandas_df.exog.values,
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3,
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hasconst=False,
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swap=False,
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)
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def test_results(self):
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stata_results = np.array([-158.7504, -83.29674, 162.9978, -238.4515])
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stata_results_pooled = np.array([-158.7504, -130.8095, -27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOaxacaPandasNoConstPassed:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(
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pandas_df.endog, pandas_df.exog, "OWNRENT", hasconst=False
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)
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def test_results(self):
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stata_results = np.array([158.7504, 321.7482, 75.45371, -238.4515])
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stata_results_pooled = np.array([158.7504, 130.8095, 27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOaxacaPandasNoSwapNoConstPassed:
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@classmethod
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def setup_class(cls):
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cls.model = OaxacaBlinder(
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pandas_df.endog,
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pandas_df.exog,
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"OWNRENT",
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hasconst=False,
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swap=False,
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)
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def test_results(self):
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stata_results = np.array([-158.7504, -83.29674, 162.9978, -238.4515])
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stata_results_pooled = np.array([-158.7504, -130.8095, -27.94091])
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endow, coef, inter, gap = self.model.three_fold().params
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unexp, exp, gap = self.model.two_fold().params
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np.testing.assert_almost_equal(gap, stata_results[0], 3)
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np.testing.assert_almost_equal(endow, stata_results[1], 3)
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np.testing.assert_almost_equal(coef, stata_results[2], 3)
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np.testing.assert_almost_equal(inter, stata_results[3], 3)
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np.testing.assert_almost_equal(gap, stata_results_pooled[0], 3)
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np.testing.assert_almost_equal(exp, stata_results_pooled[1], 3)
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np.testing.assert_almost_equal(unexp, stata_results_pooled[2], 3)
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class TestOneModel:
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@classmethod
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def setup_class(cls):
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np.random.seed(0)
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cls.one_model = OaxacaBlinder(
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pandas_df.endog, pandas_df.exog, "OWNRENT", hasconst=False
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).two_fold(True, two_fold_type="self_submitted", submitted_weight=1)
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def test_results(self):
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unexp, exp, gap = self.one_model.params
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unexp_std, exp_std = self.one_model.std
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one_params_stata_results = np.array([75.45370, 83.29673, 158.75044])
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one_std_stata_results = np.array([64.58479, 71.05619])
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np.testing.assert_almost_equal(unexp, one_params_stata_results[0], 3)
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np.testing.assert_almost_equal(exp, one_params_stata_results[1], 3)
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np.testing.assert_almost_equal(gap, one_params_stata_results[2], 3)
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np.testing.assert_almost_equal(unexp_std, one_std_stata_results[0], 3)
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np.testing.assert_almost_equal(exp_std, one_std_stata_results[1], 3)
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class TestZeroModel:
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@classmethod
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def setup_class(cls):
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np.random.seed(0)
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cls.zero_model = OaxacaBlinder(
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pandas_df.endog, pandas_df.exog, "OWNRENT", hasconst=False
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).two_fold(True, two_fold_type="self_submitted", submitted_weight=0)
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def test_results(self):
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unexp, exp, gap = self.zero_model.params
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unexp_std, exp_std = self.zero_model.std
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zero_params_stata_results = np.array([-162.9978, 321.7482, 158.75044])
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zero_std_stata_results = np.array([668.1512, 653.10389])
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np.testing.assert_almost_equal(unexp, zero_params_stata_results[0], 3)
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np.testing.assert_almost_equal(exp, zero_params_stata_results[1], 3)
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np.testing.assert_almost_equal(gap, zero_params_stata_results[2], 3)
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np.testing.assert_almost_equal(unexp_std, zero_std_stata_results[0], 3)
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np.testing.assert_almost_equal(exp_std, zero_std_stata_results[1], 3)
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class TestOmegaModel:
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@classmethod
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def setup_class(cls):
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np.random.seed(0)
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cls.omega_model = OaxacaBlinder(
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pandas_df.endog, pandas_df.exog, "OWNRENT", hasconst=False
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).two_fold(True, two_fold_type="nuemark")
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def test_results(self):
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unexp, exp, gap = self.omega_model.params
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unexp_std, exp_std = self.omega_model.std
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nue_params_stata_results = np.array([19.52467, 139.22577, 158.75044])
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nue_std_stata_results = np.array([59.82744, 48.25425])
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np.testing.assert_almost_equal(unexp, nue_params_stata_results[0], 3)
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np.testing.assert_almost_equal(exp, nue_params_stata_results[1], 3)
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np.testing.assert_almost_equal(gap, nue_params_stata_results[2], 3)
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np.testing.assert_almost_equal(unexp_std, nue_std_stata_results[0], 3)
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np.testing.assert_almost_equal(exp_std, nue_std_stata_results[1], 3)
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class TestPooledModel:
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@classmethod
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def setup_class(cls):
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np.random.seed(0)
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cls.pooled_model = OaxacaBlinder(
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pandas_df.endog, pandas_df.exog, "OWNRENT", hasconst=False
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).two_fold(True)
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def test_results(self):
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unexp, exp, gap = self.pooled_model.params
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unexp_std, exp_std = self.pooled_model.std
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pool_params_stata_results = np.array(
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[27.940908, 130.809536, 158.75044]
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)
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pool_std_stata_results = np.array([89.209487, 58.612367])
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np.testing.assert_almost_equal(unexp, pool_params_stata_results[0], 3)
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np.testing.assert_almost_equal(exp, pool_params_stata_results[1], 3)
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np.testing.assert_almost_equal(gap, pool_params_stata_results[2], 3)
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np.testing.assert_almost_equal(unexp_std, pool_std_stata_results[0], 3)
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np.testing.assert_almost_equal(exp_std, pool_std_stata_results[1], 3)
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