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

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...

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Autores:

Rios-Avila, Fernando
Canavire-Bacarreza, Gustavo

Tipo de recurso:

Fecha de publicación:

2017

Institución:

Universidad EAFIT

Repositorio:

Repositorio EAFIT

Idioma:

eng

OAI Identifier:

oai:repository.eafit.edu.co:10784/11432

Acceso en línea:

http://hdl.handle.net/10784/11432

Palabra clave:

Maximum Likelihood Estimation
non-linear models
endogeneity
two-step models
standard errors

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