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Self-selection and direct estimation of across-regime correlation parameter

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  • Giorgio Calzolari
  • Antonino Di Pino

Abstract

A direct maximum likelihood (ML) procedure to estimate the ‘generally unidentified’ across-regime correlation parameter in a two-regime endogenous switching model is here provided. The results of a Monte Carlo experiment confirm consistency of our direct ML procedure, and its relative efficiency over widely applied models and methods. As an empirical application, we estimate a two-regime simultaneous equation model of domestic work of Italian married women in which the two regimes are given by their working status (employed or unemployed).

Suggested Citation

  • Giorgio Calzolari & Antonino Di Pino, 2017. "Self-selection and direct estimation of across-regime correlation parameter," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2142-2160, September.
  • Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2142-2160
    DOI: 10.1080/02664763.2016.1247789
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    References listed on IDEAS

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    1. Vijverberg, Wim P. M., 1993. "Measuring the unidentified parameter of the extended Roy model of selectivity," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 69-89.
    2. Chen, Heng & Fan, Yanqin & Liu, Ruixuan, 2016. "Inference for the correlation coefficient between potential outcomes in the Gaussian switching regime model," Journal of Econometrics, Elsevier, vol. 195(2), pages 255-270.
    3. Poirier, Dale J & Tobias, Justin L, 2003. "On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 258-268, April.
    4. Maddala, G S & Nelson, Forrest D, 1974. "Maximum Likelihood Methods for Models of Markets in Disequilibrium," Econometrica, Econometric Society, vol. 42(6), pages 1013-1030, November.
    5. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
    6. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
    7. Vella, Francis & Verbeek, Marno, 1999. "Estimating and Interpreting Models with Endogenous Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 473-478, October.
    8. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    9. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-1149, September.
    10. Michael Lokshin & Zurab Sajaia, 2004. "Maximum likelihood estimation of endogenous switching regression models," Stata Journal, StataCorp LP, vol. 4(3), pages 282-289, September.
    11. Poirier, Dale J. & Ruud, Paul A., 1981. "On the appropriateness of endogenous switching," Journal of Econometrics, Elsevier, vol. 16(2), pages 249-256, June.
    12. James Heckman & Justin L. Tobias & Edward Vytlacil, 2003. "Simple Estimators for Treatment Parameters in a Latent-Variable Framework," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 748-755, August.
    13. Lee, Lung-Fei, 1978. "Unionism and Wage Rates: A Simultaneous Equations Model with Qualitative and Limited Dependent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 19(2), pages 415-433, June.
    14. Maria Gabriella Campolo & Antonio Di Pino, 2012. "An Empirical Analysis of Women’s Working Time, and an Estimation of Female Labour Supply in Italy," Statistica, Department of Statistics, University of Bologna, vol. 72(2), pages 173-193.
    15. Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 15-51.
    16. Suzanne Bianchi, 2000. "Maternal employment and time with children: Dramatic change or surprising continuity?," Demography, Springer;Population Association of America (PAA), vol. 37(4), pages 401-414, November.
    17. Calzolari, Giorgio & Panattoni, Lorenzo, 1988. "Alternative Estimators of FIML Covariance Matrix: A Monte Carlo Stud y," Econometrica, Econometric Society, vol. 56(3), pages 701-714, May.
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    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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