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

2017-05-232017-05-01https://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.application/pdfengStandard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approachinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/openAccessMaximum Likelihood Estimationnon-linear modelsendogeneitytwo-step modelsstandard errorsAcceso abierto2017-05-23Rios-Avila, FernandoCanavire-Bacarreza, Gustavoreponame:Repositorio Institucional Universidad EAFITinstname:Universidad EAFITrepourl:https://repository.eafit.edu.co