Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach
Fernando Rios-Avila
Levy Economics Institute
Annandale-on-Hudson, NY
[email protected]
|
Gustavo Canavire-Bacarreza
School of Economics and Finance
Universidad EAFIT
Medellín, Colombia
[email protected]
|
Abstract. Following Wooldridge (2014, Journal of Econometrics 182: 226–234),
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 the proposed method
with routines already implemented in Stata to deal with selection and
endogeneity problems. This strategy is an alternative to the use of bootstrap
methods and has the advantage that it can be easily applied for the estimation
of two-stage optimization models for which already built-in programs are not
yet available. It could be of particular use for addressing endogeneity in a
nonlinear framework.
View all articles by these authors:
Fernando Rios-Avila, Gustavo Canavire-Bacarreza
View all articles with these keywords:
maximum likelihood estimation, nonlinear models, endogeneity, two-step models, standard errors
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
