import wooldridge as woo

import numpy as np

import pandas as pd

import statsmodels.api as sm

import statsmodels.formula.api as smf

import patsy as pt

hprice1 = woo.dataWoo('hprice1')

# estimate model:

reg = smf.ols(formula='np.log(price) ~ np.log(lotsize) + np.log(sqrft) + bdrms',

data=hprice1)

results = reg.fit()

# BP test:

y, X_bp = pt.dmatrices('np.log(price) ~ np.log(lotsize) + np.log(sqrft) + bdrms',

data=hprice1, return_type='dataframe')

result_bp = sm.stats.diagnostic.het_breuschpagan(results.resid, X_bp)

bp_statistic = result_bp[0]

bp_pval = result_bp[1]

print(f'bp_statistic: {bp_statistic}\n')

print(f'bp_pval: {bp_pval}\n')

# White test:

X_wh = pd.DataFrame({'const': 1, 'fitted_reg': results.fittedvalues,

'fitted_reg_sq': results.fittedvalues ** 2})

result_white = sm.stats.diagnostic.het_breuschpagan(results.resid, X_wh)

white_statistic = result_white[0]

white_pval = result_white[1]

print(f'white_statistic: {white_statistic}\n')

print(f'white_pval: {white_pval}\n')