Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
IDEAS home Printed from https://ideas.repec.org/p/boc/scon21/22.html
   My bibliography  Save this paper

Estimation of ordered probit model with endogenous switching between two latent regimes

Author

Listed:
  • Jan Willem Nijenhuis

    (University of Amsterdam)

Abstract

Ordinal responses can be generated, in the time-series context, by different latent regimes or, in the cross-sectional context, by different unobserved groups of population. These latent classes or states can distort the inference in a traditional single-equation model. Finite mixture or regime switching models surmount the problem of unobserved heterogeneity or clustering through their flexible form. The available Stata command for finite mixture of ordered probit models, fmm: oprobit, does not allow for endogenous switching, when the unobservables in the switching equation are correlated with the unobservables in the outcome equations. We introduce two new commands, swopit and swopitc, that fit a switching ordered probit model for ordered choices with exogenous and endogenous switching between two unobserved regimes or groups. We provide a battery of postestimation commands, access the small-sample performance of the maximum likelihood estimator of the parameters and the bootstrap estimator of standard errors by Monte Carlo experiments, and apply the new commands to model the policy interest rates and health status responses.

Suggested Citation

  • Jan Willem Nijenhuis, 2021. "Estimation of ordered probit model with endogenous switching between two latent regimes," 2021 Stata Conference 22, Stata Users Group.
    • Handle: RePEc:boc:scon21:22
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/scon2021/US21_Nijenhuis1.pdf
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/scon2021/US21_Nijenhuis2.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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 of Labor Economics (IZA).
    2. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    3. Gronau, Reuben, 1974. "Wage Comparisons-A Selectivity Bias," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1119-1143, Nov.-Dec..
    4. Harris, Mark N. & Zhao, Xueyan, 2007. "A zero-inflated ordered probit model, with an application to modelling tobacco consumption," Journal of Econometrics, Elsevier, vol. 141(2), pages 1073-1099, December.
    5. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    6. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    7. John Geweke & Gautam Gowrisankaran & Robert J. Town, 2003. "Bayesian Inference for Hospital Quality in a Selection Model," Econometrica, Econometric Society, vol. 71(4), pages 1215-1238, July.
    8. Munkin, Murat K. & Trivedi, Pravin K., 2008. "Bayesian analysis of the ordered probit model with endogenous selection," Journal of Econometrics, Elsevier, vol. 143(2), pages 334-348, April.
    9. Hill, Daniel H, 1990. "An Endogenously-Switching Ordered-Response Model of Information, Eligibility and Participation in SSI," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 368-371, May.
    10. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    11. Takuya Hasebe, 2020. "Endogenous switching regression model and treatment effects of count-data outcome," Stata Journal, StataCorp LP, vol. 20(3), pages 627-646, September.
    12. Lewis, H Gregg, 1974. "Comments on Selectivity Biases in Wage Comparisons," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1145-1155, Nov.-Dec..
    13. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
    14. Christian A. Gregory, 2015. "Estimating treatment effects for ordered outcomes using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 15(3), pages 756-774, September.
    15. Alfonso Miranda & Sophia Rabe-Hesketh, 2006. "Maximum likelihood estimation of endogenous switching and sample selection models for binary, ordinal, and count variables," Stata Journal, StataCorp LP, vol. 6(3), pages 285-308, September.
    16. Christian Gregory, 2015. "Estimating Treatment Effects for Ordered Outcomes Using Maximum Simulated Likelihood," 2015 Stata Conference 2, Stata Users Group.
    17. Chib, Siddhartha & Hamilton, Barton H., 2000. "Bayesian analysis of cross-section and clustered data treatment models," Journal of Econometrics, Elsevier, vol. 97(1), pages 25-50, July.
    18. Sirchenko Andrei, 2020. "A model for ordinal responses with heterogeneous status quo outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-16, February.
    19. Sirchenko Andrei, 2020. "A model for ordinal responses with heterogeneous status quo outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-16, February.
    20. Takuya Hasebe, 2018. "Treatment effect estimators for count data models," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1868-1873, November.
    21. Wilde, Joachim, 2000. "Identification of multiple equation probit models with endogenous dummy regressors," Economics Letters, Elsevier, vol. 69(3), pages 309-312, December.
    22. William H. Greene & Mark N. Harris & Bruce Hollingworth & Pushkar Maitra, 2008. "A Bivariate Latent Class Correlated Generalized Ordered Probit Model with an Application to Modeling Observed Obesity Levels," Working Papers 08-18, New York University, Leonard N. Stern School of Business, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.
    2. James J. Heckman, 2005. "Micro Data, Heterogeneity and the Evaluation of Public Policy Part 2," The American Economist, Sage Publications, vol. 49(1), pages 16-44, March.
    3. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
    4. Cunha, Flavio & Heckman, James J., 2007. "Identifying and Estimating the Distributions of Ex Post and Ex Ante Returns to Schooling," Labour Economics, Elsevier, vol. 14(6), pages 870-893, December.
    5. Quinones, Esteban J. & Barham, Bradford L., 2018. "Endogenous Selection, Migration and Occupation Outcomes for Rural Southern Mexicans," Staff Paper Series 587, University of Wisconsin, Agricultural and Applied Economics.
    6. Maarten Goos & Anna Salomons, 2017. "Measuring teaching quality in higher education: assessing selection bias in course evaluations," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(4), pages 341-364, June.
    7. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2010. "Measuring heterogeneity in the returns to education using an education reform," European Economic Review, Elsevier, vol. 54(4), pages 483-500, May.
    8. Favara, Marta, 2012. "The Cost of Acting "Girly": Gender Stereotypes and Educational Choices," IZA Discussion Papers 7037, Institute of Labor Economics (IZA).
    9. Marra Giampiero & Radice Rosalba, 2017. "A joint regression modeling framework for analyzing bivariate binary data in R," Dependence Modeling, De Gruyter, vol. 5(1), pages 268-294, December.
    10. Jeremy T. Fox, 2010. "Estimating the Employer Switching Costs and Wage Responses of Forward-Looking Engineers," Journal of Labor Economics, University of Chicago Press, vol. 28(2), pages 357-412, April.
    11. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    12. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2003. "Measuring Heterogeneity in the Returns to Education in Norway Using Educational Reforms," IZA Discussion Papers 815, Institute of Labor Economics (IZA).
    13. Joshua C. C. Chan & Justin L. Tobias, 2015. "Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 650-674, June.
    14. D. Fabbri & C. Monfardini & R. Radice, 2004. "Testing exogeneity in the bivariate probit model: Monte Carlo evidence and an application to health economics," Working Papers 514, Dipartimento Scienze Economiche, Universita' di Bologna.
    15. 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.
    16. Robert Moffitt, 1991. "Program Evaluation With Nonexperimental Data," Evaluation Review, , vol. 15(3), pages 291-314, June.
    17. Claudia Olivetti & Barbara Petrongolo, 2008. "Unequal Pay or Unequal Employment? A Cross-Country Analysis of Gender Gaps," Journal of Labor Economics, University of Chicago Press, vol. 26(4), pages 621-654, October.
    18. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    19. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    20. D’Haultfoeuille, Xavier & Maurel, Arnaud, 2013. "Another Look At The Identification At Infinity Of Sample Selection Models," Econometric Theory, Cambridge University Press, vol. 29(1), pages 213-224, February.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:scon21:22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.