import wooldridge as wooimport pandas as pdimport statsmodels.formula.api as smfnyse = woo.dataWoo('nyse')nyse['ret'] = nyse['return']nyse['ret_lag1'] = nyse['ret'].shift(1)# linear regression of model:reg = smf.ols(formula='ret ~ ret_lag1', data=nyse)results = reg.fit()# squared residuals:nyse['resid_sq'] = results.resid ** 2nyse['resid_sq_lag1'] = nyse['resid_sq'].shift(1)# model for squared residuals:ARCHreg = smf.ols(formula='resid_sq ~ resid_sq_lag1', data=nyse)results_ARCH = ARCHreg.fit()# print regression table:table = pd.DataFrame({'b': round(results_ARCH.params, 4),'se': round(results_ARCH.bse, 4),'t': round(results_ARCH.tvalues, 4),'pval': round(results_ARCH.pvalues, 4)})print(f'table: \n{table}\n')
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