Thanks for your work Gabriele. So to clarify, what does the first stage residual option produce? Technical efficiency?
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y_i,t = alpha + w_i,t*beta + k_i,t*gamma + h(inv_i,t , k_i,t ) + epsilon_i,t
newvar = y_i,t - \hat{y}_i,t = \hat{epsilon}_i,t
prodest log_y, free(log_lab1 log_lab2) state(log_k) proxy(log_investment) va met(op) poly(4) reps(40) id(id) t(year) control(i.year) ## NOT WORKING ## qui tab year, gen(dy) // generate one dummy per year prodest log_y, free(log_lab1 log_lab2) state(log_k) proxy(log_investment) va met(op) poly(4) reps(40) id(id) t(year) control(dy*)
g tfp = . forv g = 1 / G #total number of groups#{ tempvar tfp`g' prodest log_y if group == `g', free(log_lab1 log_lab2) state(log_k) proxy(log_materials) va met(lp) opt(dfp) reps(50) id(id) t(year) # change with your model predict `tfp`g'', residuals replace tfp = `tfp`g'' if group == `g' }
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