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')