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

Fernando Rios-Avila () and Gustavo Canavire-Bacarreza

No 15659, Documentos de Trabajo de Valor Público from Universidad EAFIT

Abstract: 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.

Keywords: MaximumLikelihood Estimation; non-linearmodels; endogeneity; two-step models; standard errors (search for similar items in EconPapers)
Pages: 22
Date: 2017-05-01
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https://repository.eafit.edu.co/handle/10784/11432#.WWk0etPyvq0

Related works:
Journal Article: Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:col:000122:015659

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