This replicates Table 2.2 in Breen (1996).
clear // Simulate data set obs 2000 generate ui = rnormal(0, 2) generate xi = rnormal() generate yi_star = 1 + 2*xi + ui drop ui // Fit OLS model regress yi_star xi eststo ols1 estadd scalar sigma = e(rmse) // Truncate variable generate yi = yi_star if yi_star > 0 replace yi = 0 if yi_star <= 0 recode yi (0 = .), gen(yi_h) // (A) OLS (using all observations on y // including y1 = 0) regress yi xi eststo ols2 estadd scalar sigma = e(rmse) // (B) OLS (yi > 0) regress yi xi if yi >0 eststo ols3 estadd scalar sigma = e(rmse) // (C) Heckman 2-step heckman yi_h xi, select(xi) twostep eststo heckman // (D) Tobit tobit yi xi, ll(0) eststo tobit estadd scalar sigma = _b[sigma:_cons] // Table 2.2esttab ols2 ols3 heckman tobit ols1, b(3) se(3) nostar stat(sigma) /// mtitles("(A) OLS incl. yi = 0" /// "(B) OLS yi > 0" /// "(C) Heckman 2-step" /// "(D) Tobit" /// "OLS yi_star") /// coeflabel(_cons "alpha" xi "beta") /// collabels() /// drop(mills:lambda sigma:_cons) /// order(_cons xi) /// unstack /// modelwidth(20) nonumber
