Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach

2017-05-232017-05-01http://hdl.handle.net/10784/11432Following 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.engStandard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation ApproachworkingPaperinfo:eu-repo/semantics/openAccessMaximum Likelihood Estimationnon-linear modelsendogeneitytwo-step modelsstandard errorsAcceso abierto2017-05-23Rios-Avila, FernandoCanavire-Bacarreza, Gustavo