<filename>py4etrics/py4etrics/hetero_test.py
'''
Specification test for heteroscedasticity in Logit/Probit models

The tests are based on
(1) Wooldridge 2010, section 15.5.3
(2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models

Check consistency with Stata using the following

----- Stata code starts -----

// https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models
// https://www.stata.com/manuals13/p_predict.pdf
// https://www.stata.com/manuals13/rpredict.pdf

import delimited "/Users/Tetsu/Documents/My_Simulation/Python/projects/wooldridge/raw_data/data_csv/mroz.csv"

probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6

predict xbhat, xb
// the following give s the same resutl
// predict xbhat, index

// 'c' for continuous variables for interaction term
// '#' multiplication for interaction terms
probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 c.xbhat#c.nwifeinc c.xbhat#c.educ c.xbhat#c.exper c.xbhat#c.expersq c.xbhat#c.age c.xbhat#c.kidslt6 c.xbhat#c.kidsge6

test c.xbhat#c.nwifeinc c.xbhat#c.educ c.xbhat#c.exper c.xbhat#c.expersq c.xbhat#c.age c.xbhat#c.kidslt6 c.xbhat#c.kidsge6

// for reference ---------------------------------------------------------------
hetprobit inlf nwifeinc educ exper expersq age kidslt6 kidsge6, het(nwifeinc educ exper expersq age kidslt6 kidsge6)

----- Stata code ends -----

Created by <NAME>
Modified: 12 May 2020
'''


import numpy as np
from scipy import stats
import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import logit, probit


def het_test_logit(results):
    """
    Wald Test for Logit
    -------------------
    H0: homoscedasticity
    HA: heteroscedasticity

    Parameters
    ----------
    results : Logit results instance


    Returns
    -------
    Wald test statistic
    p-value
    Degree of Freedom
        The number of restrictions, which are equivalent to the number of explanatory variables, excluding a constant term

    References
    ----------
    The test is based on
    (1) Wooldridge 2010, section 15.5.3
    (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models
    """

    yhat = results.predict(linear=True)  # original fitted values
    exog_var = results.model.exog  # original exog
    exog_df = pd.DataFrame(exog_var)  # convert to DataFrame

    try:  # drop a column of a constant if any
        tt = exog_df.nunique()
        idx_1 = list(tt).index(1.0)
        exog_df = exog_df.drop(idx_1, axis=1)
    except ValueError:
        pass

    num_para = exog_df.shape[1] # no of non-constant parameters

    # X = np.exp(yhat).reshape(len(yhat),1) * exog_df.values
    X = yhat.reshape(len(yhat),1) * exog_df.values

    endog = results.model.endog
    exog = np.column_stack((results.model.exog, X))
    res_test = sm.Logit(endog,exog).fit(disp=False)

    A = np.identity(len(res_test.params))
    A = A[-num_para:,:]
    s = res_test.wald_test(A)
    return print('H0: homoscedasticity\nHA: heteroscedasticity\n',
                '\nWald test:', "%#2.3f" % s.statistic[0][0],
                '\np-value:', "%#7.3f" % s.pvalue, '\ndf freedom:', "%#3.0f" % s.df_denom)





def het_test_probit(results):
    """
    Wald検定 for Probit
    ------------------
    H0: homoscedasticity
    HA: heteroscedasticity

    Parameters
    ----------
    results : Logit results instance

    Returns
    -------
    Wald test statistic
    p-value
    Degree of Freedom
        The number of restrictions, which are equivalent to the number of explanatory variables, excluding a constant term

    References
    ----------
    The test is based on
    (1) Wooldridge 2010, section 15.5.3
    (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models
    """

    yhat = results.predict(linear=True)  # original fitted values
    exog_var = results.model.exog  # original exog
    exog_df = pd.DataFrame(exog_var)  # convert to DataFrame

    try:  # drop a column of a constant if any
        tt = exog_df.nunique()
        idx_1 = list(tt).index(1.0)
        exog_df = exog_df.drop(idx_1, axis=1)
    except ValueError:
        pass

    num_para = exog_df.shape[1] # no of non-constant parameters

    # X = np.exp(yhat).reshape(len(yhat),1) * exog_df.values
    X = yhat.reshape(len(yhat),1) * exog_df.values

    endog = results.model.endog
    exog = np.column_stack((results.model.exog, X))
    res_test = sm.Probit(endog,exog).fit(disp=False)

    A = np.identity(len(res_test.params))
    A = A[-num_para:,:]
    s = res_test.wald_test(A)
    return print('H0: homoscedasticity\nHA: heteroscedasticity\n',
                '\nWald test:', "%#2.3f" % s.statistic[0][0],
                '\np-value:', "%#7.3f" % s.pvalue, '\ndf freedom:', "%#3.0f" % s.df_denom)

<filename>py4etrics/py4etrics/hetero_test.py
'''
Specification test for heteroscedasticity in Logit/Probit models

The tests are based on
(1) Wooldridge 2010, section 15.5.3
(2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models

Check consistency with Stata using the following

----- Stata code starts -----

// https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models
// https://www.stata.com/manuals13/p_predict.pdf
// https://www.stata.com/manuals13/rpredict.pdf

import delimited "/Users/Tetsu/Documents/My_Simulation/Python/projects/wooldridge/raw_data/data_csv/mroz.csv"

probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6

predict xbhat, xb
// the following give s the same resutl
// predict xbhat, index

// 'c' for continuous variables for interaction term
// '#' multiplication for interaction terms
probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 c.xbhat#c.nwifeinc c.xbhat#c.educ c.xbhat#c.exper c.xbhat#c.expersq c.xbhat#c.age c.xbhat#c.kidslt6 c.xbhat#c.kidsge6

test c.xbhat#c.nwifeinc c.xbhat#c.educ c.xbhat#c.exper c.xbhat#c.expersq c.xbhat#c.age c.xbhat#c.kidslt6 c.xbhat#c.kidsge6

// for reference ---------------------------------------------------------------
hetprobit inlf nwifeinc educ exper expersq age kidslt6 kidsge6, het(nwifeinc educ exper expersq age kidslt6 kidsge6)

----- Stata code ends -----

Created by <NAME>
Modified: 12 May 2020
'''


import numpy as np
from scipy import stats
import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import logit, probit


def het_test_logit(results):
    """
    Wald Test for Logit
    -------------------
    H0: homoscedasticity
    HA: heteroscedasticity

    Parameters
    ----------
    results : Logit results instance


    Returns
    -------
    Wald test statistic
    p-value
    Degree of Freedom
        The number of restrictions, which are equivalent to the number of explanatory variables, excluding a constant term

    References
    ----------
    The test is based on
    (1) Wooldridge 2010, section 15.5.3
    (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models
    """

    yhat = results.predict(linear=True)  # original fitted values
    exog_var = results.model.exog  # original exog
    exog_df = pd.DataFrame(exog_var)  # convert to DataFrame

    try:  # drop a column of a constant if any
        tt = exog_df.nunique()
        idx_1 = list(tt).index(1.0)
        exog_df = exog_df.drop(idx_1, axis=1)
    except ValueError:
        pass

    num_para = exog_df.shape[1] # no of non-constant parameters

    # X = np.exp(yhat).reshape(len(yhat),1) * exog_df.values
    X = yhat.reshape(len(yhat),1) * exog_df.values

    endog = results.model.endog
    exog = np.column_stack((results.model.exog, X))
    res_test = sm.Logit(endog,exog).fit(disp=False)

    A = np.identity(len(res_test.params))
    A = A[-num_para:,:]
    s = res_test.wald_test(A)
    return print('H0: homoscedasticity\nHA: heteroscedasticity\n',
                '\nWald test:', "%#2.3f" % s.statistic[0][0],
                '\np-value:', "%#7.3f" % s.pvalue, '\ndf freedom:', "%#3.0f" % s.df_denom)





def het_test_probit(results):
    """
    Wald検定 for Probit
    ------------------
    H0: homoscedasticity
    HA: heteroscedasticity

    Parameters
    ----------
    results : Logit results instance

    Returns
    -------
    Wald test statistic
    p-value
    Degree of Freedom
        The number of restrictions, which are equivalent to the number of explanatory variables, excluding a constant term

    References
    ----------
    The test is based on
    (1) Wooldridge 2010, section 15.5.3
    (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models
    """

    yhat = results.predict(linear=True)  # original fitted values
    exog_var = results.model.exog  # original exog
    exog_df = pd.DataFrame(exog_var)  # convert to DataFrame

    try:  # drop a column of a constant if any
        tt = exog_df.nunique()
        idx_1 = list(tt).index(1.0)
        exog_df = exog_df.drop(idx_1, axis=1)
    except ValueError:
        pass

    num_para = exog_df.shape[1] # no of non-constant parameters

    # X = np.exp(yhat).reshape(len(yhat),1) * exog_df.values
    X = yhat.reshape(len(yhat),1) * exog_df.values

    endog = results.model.endog
    exog = np.column_stack((results.model.exog, X))
    res_test = sm.Probit(endog,exog).fit(disp=False)

    A = np.identity(len(res_test.params))
    A = A[-num_para:,:]
    s = res_test.wald_test(A)
    return print('H0: homoscedasticity\nHA: heteroscedasticity\n',
                '\nWald test:', "%#2.3f" % s.statistic[0][0],
                '\np-value:', "%#7.3f" % s.pvalue, '\ndf freedom:', "%#3.0f" % s.df_denom)

Specification test for heteroscedasticity in Logit/Probit models  The tests are based on (1) Wooldridge 2010, section 15.5.3 (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models  Check consistency with Stata using the following  ----- Stata code starts -----  // https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models // https://www.stata.com/manuals13/p_predict.pdf // https://www.stata.com/manuals13/rpredict.pdf  import delimited "/Users/Tetsu/Documents/My_Simulation/Python/projects/wooldridge/raw_data/data_csv/mroz.csv"  probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6  predict xbhat, xb // the following give s the same resutl // predict xbhat, index  // 'c' for continuous variables for interaction term // '#' multiplication for interaction terms probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 c.xbhat#c.nwifeinc c.xbhat#c.educ c.xbhat#c.exper c.xbhat#c.expersq c.xbhat#c.age c.xbhat#c.kidslt6 c.xbhat#c.kidsge6  test c.xbhat#c.nwifeinc c.xbhat#c.educ c.xbhat#c.exper c.xbhat#c.expersq c.xbhat#c.age c.xbhat#c.kidslt6 c.xbhat#c.kidsge6  // for reference --------------------------------------------------------------- hetprobit inlf nwifeinc educ exper expersq age kidslt6 kidsge6, het(nwifeinc educ exper expersq age kidslt6 kidsge6)  ----- Stata code ends -----  Created by <NAME> Modified: 12 May 2020 Wald Test for Logit     -------------------     H0: homoscedasticity     HA: heteroscedasticity      Parameters     ----------     results : Logit results instance       Returns     -------     Wald test statistic     p-value     Degree of Freedom         The number of restrictions, which are equivalent to the number of explanatory variables, excluding a constant term      References     ----------     The test is based on     (1) Wooldridge 2010, section 15.5.3     (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models # original fitted values # original exog # convert to DataFrame # drop a column of a constant if any # no of non-constant parameters # X = np.exp(yhat).reshape(len(yhat),1) * exog_df.values #2.3f" % s.statistic[0][0], #7.3f" % s.pvalue, '\ndf freedom:', "%#3.0f" % s.df_denom) Wald検定 for Probit     ------------------     H0: homoscedasticity     HA: heteroscedasticity      Parameters     ----------     results : Logit results instance      Returns     -------     Wald test statistic     p-value     Degree of Freedom         The number of restrictions, which are equivalent to the number of explanatory variables, excluding a constant term      References     ----------     The test is based on     (1) Wooldridge 2010, section 15.5.3     (2) https://www.statalist.org/forums/forum/general-stata-discussion/general/1292180-test-for-heteroskedasticity-in-logit-probit-models # original fitted values # original exog # convert to DataFrame # drop a column of a constant if any # no of non-constant parameters # X = np.exp(yhat).reshape(len(yhat),1) * exog_df.values #2.3f" % s.statistic[0][0], #7.3f" % s.pvalue, '\ndf freedom:', "%#3.0f" % s.df_denom)