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Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach

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  • Fernando Rios-Avila

    (Levy Economics Institute)

  • Gustavo Canavire-Bacarreza

    (Universidad EAFIT)

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. Specif- ically, 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.

Suggested Citation

  • Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2018. "Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach," Stata Journal, StataCorp LP, vol. 18(1), pages 206-222, March.
    • Handle: RePEc:tsj:stataj:v:18:y:2018:i:1:p:206-222
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    References listed on IDEAS

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    1. James W. Hardin, 2002. "The robust variance estimator for two-stage models," Stata Journal, StataCorp LP, vol. 2(3), pages 253-266, August.
    2. Newey, Whitney K., 1987. "Efficient estimation of limited dependent variable models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 36(3), pages 231-250, November.
    3. Arne Risa Hole, 2006. "Calculating Murphy-Topel variance estimates in Stata: A simplified procedure," Stata Journal, StataCorp LP, vol. 6(4), pages 521-529, December.
    4. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    5. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    6. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    8. John Mullahy, 1997. "Instrumental-Variable Estimation Of Count Data Models: Applications To Models Of Cigarette Smoking Behavior," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 586-593, November.
    9. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
    10. Wooldridge, Jeffrey M., 2014. "Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 182(1), pages 226-234.
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    Cited by:

    1. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2020. "The Effect of Immigration on Labor Market Transitions of Native-Born Unemployed in the United States," Journal of Labor Research, Springer, vol. 41(3), pages 295-331, September.
    2. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 0. "The Effect of Immigration on Labor Market Transitions of Native-Born Unemployed in the United States," Journal of Labor Research, Springer, vol. 0, pages 1-37.

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