import wooldridge as wooimport pandas as pdimport linearmodels as plmwagepan = woo.dataWoo('wagepan')wagepan['t'] = wagepan['year']wagepan['entity'] = wagepan['nr']wagepan = wagepan.set_index(['nr'])# include group specific means:wagepan['married_b'] = wagepan.groupby('nr').mean()['married']wagepan['union_b'] = wagepan.groupby('nr').mean()['union']wagepan = wagepan.set_index(['year'], append=True)# estimate CRE paramters:reg = plm.RandomEffects.from_formula(formula='lwage ~ married + union + educ +''black + hisp + married_b + union_b',data=wagepan)results = reg.fit()# print regression table:table = pd.DataFrame({'b': round(results.params, 4),'se': round(results.std_errors, 4),'t': round(results.tstats, 4),'pval': round(results.pvalues, 4)})print(f'table: \n{table}\n')
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