I am running regressions by industry and year groups where the number of firms in the industry and year group is 25 firms.
I want to calculate the coefficients, t statistic and one-sided p values for all coefficients except for the intercept.
The coefficients on the variables atoW and lag_accW are predicted by to negative whereas the coefficents on all other variables are predicted to be positive.
I did the following (note that I was not ssure what to do with the negative coefficients' variables and hence my code for the p-value for these did not change):
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forval j = 0/6 { gen b`j'=. gen t_stat`j'=. gen p_value`j'=. } gen adjr2=. gen unce=. levelsof sic_2, local(levels) foreach x of local levels { foreach z of numlist 1990/2016 { capture reg ceW lag_ceW atoW lag_accW accW dsaleW ndsaleW if N_firms_sic_2_yr>25 & sic_2==`x' & yr==`z' if _rc == 0 { predict residual, res replace unce=residual if e(sample) drop residual replace b0 = _b[_cons] if e(sample) replace t_stat0 = b0/_se[_cons] if e(sample) replace p_value0= 2*ttail(e(df_r),abs(t_stat0)) if e(sample) replace b1 = _b[lag_ceW] if e(sample) replace t_stat1 = b1/_se[lag_ceW] if e(sample) replace p_value1= ttail(e(df_r),abs(t_stat1)) if e(sample) replace b2 = _b[atoW] if e(sample) replace t_stat2 = b2/_se[atoW] if e(sample) replace p_value2= ttail(e(df_r),abs(t_stat2)) if e(sample) replace b3 = _b[lag_accW] if e(sample) replace t_stat3 = b3/_se[lag_accW] if e(sample) replace p_value3= ttail(e(df_r),abs(t_stat3)) if e(sample) replace b4 = _b[accW] if e(sample) replace t_stat4 = b4/_se[accW] if e(sample) replace p_value4= ttail(e(df_r),abs(t_stat4)) if e(sample) replace b5 = _b[dsaleW] if e(sample) replace t_stat5 = b5/_se[dsaleW] if e(sample) replace p_value5= ttail(e(df_r),abs(t_stat5)) if e(sample) replace b6 = _b[ndsaleW] if e(sample) replace t_stat6 = b6/_se[ndsaleW] if e(sample) replace p_value6= ttail(e(df_r),abs(t_stat6)) if e(sample) replace adjr2=e(r2_a) if e(sample) } } }
Thanks
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