Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
Fecha
2017-05-01
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Universidad EAFIT
Resumen
Following Wooldridge (2014), we discuss and implement in Stata an efficient maximum likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of this estimate using different non-linear routines already implemented in Stata such as ivprobit, ivtobit, ivpoisson, heckman, and ivregress.