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...
- 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
- Rights
- License
- Acceso abierto
| Summary: | 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. |
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