IDEAS home Printed from https://ideas.repec.org/p/hit/cisdps/597.html
   My bibliography  Save this paper

Semiparametric duration analysis with an endogenous binary variable: An application to hospital stays

Author

Listed:
  • Masuhara, Hiroaki

Abstract

Background: In duration analysis, we find situations where covariates are simultaneously determined along with the duration variable. Moreover, although the models based on a hazard rate do not explicitly assume heterogeneity, in applied econometrics, the possibility of omitted variables is inevitable and controlling population heterogeneity alone is inadequate. It is important to consider both heterogeneity and endogeneity in duration analysis. Objectives and methods: Explicitly assuming semiparametric correlated heterogeneity, this paper proposes an alternative robust duration model with an endogenous binary variable that generalizes the heterogeneity of both duration and endogeneity using Hermite polynomials. Under these setups, we investigate the difference between the endogenous binary variable's coefficients of the parametric and semiparametric models using the Medical Expenditure Panel Survey (MEPS) data. Results: The parameter values of the endogenous binary variable (insurance choice) are statistically significant at the 1% level; however, the values differ among the parametric and semiparametric models and the any type of insurance choice increases the length of hospital stays by 104.010% in the censored parametric model, and 182.074% in the censored semiparametric model. Compared with the parametric model, the increase of hospital stays in the semiparametric model is large. Moreover, we find that the semiparametric model a twin-peak distribution and that the contour lines differ from the usual ellipsoids of the bivariate normal density. Conclusions: When applied to the duration of hospital stays of the MEPS data, the estimated results of the semiparametric model shows a good performance. The absolute values of the endogenous binary regressor coefficients of the semiparametric models are larger than that of the parametric model. The parametric model underestimates the effect of the individual's insurance choice in our example. Moreover, the estimated densities of the semiparametric models have twin peak distribution.

Suggested Citation

  • Masuhara, Hiroaki, 2013. "Semiparametric duration analysis with an endogenous binary variable: An application to hospital stays," CIS Discussion paper series 597, Center for Intergenerational Studies, Institute of Economic Research, Hitotsubashi University.
  • Handle: RePEc:hit:cisdps:597
    as

    Download full text from publisher

    File URL: https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/25589/DP597.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    2. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    3. Hajivassiliou, V A, 1994. "A Simulation Estimation Analysis of the External Debt Crises of Developing Countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(2), pages 109-131, April-Jun.
    4. Bijwaard, Govert E. & Ridder, Geert, 2005. "Correcting for selective compliance in a re-employment bonus experiment," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 77-111.
    5. repec:ebl:ecbull:v:3:y:2008:i:42:p:1-13 is not listed on IDEAS
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    7. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
    8. Hiroaki Masuhara, 2008. "Semi-nonparametric count data estimation with an endogenous binary variable," Economics Bulletin, AccessEcon, vol. 3(42), pages 1-13.
    9. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    10. Donald S. Kenkel & Joseph V. Terza, 2001. "The effect of physician advice on alcohol consumption: count regression with an endogenous treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 165-184.
    11. van der Klaauw, Bas & Koning, Ruud H, 2003. "Testing the Normality Assumption in the Sample Selection Model with an Application to Travel Demand," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 31-42, January.
    12. Hiroaki Masuhara, 2007. "Semi-nonparametric estimation of regression-based survival models," Economics Bulletin, AccessEcon, vol. 3(61), pages 1-12.
    13. repec:ebl:ecbull:v:3:y:2007:i:61:p:1-12 is not listed on IDEAS
    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. Maruyama, Shiko, 2014. "Estimation of finite sequential games," Journal of Econometrics, Elsevier, vol. 178(2), pages 716-726.
    2. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    3. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    4. Peter Haan, 2005. "State Dependence and Female Labor Supply in Germany: The Extensive and the Intensive Margin," Discussion Papers of DIW Berlin 538, DIW Berlin, German Institute for Economic Research.
    5. Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.
    6. Schmidheiny, Kurt, 2006. "Income segregation and local progressive taxation: Empirical evidence from Switzerland," Journal of Public Economics, Elsevier, vol. 90(3), pages 429-458, February.
    7. David Roodman, 2009. "Estimating Fully Observed Recursive Mixed-Process Models with cmp," Working Papers 168, Center for Global Development.
    8. Abay, Kibrom A. & Berhane, Guush & Taffesse, Alemayehu Seyoum & Koru, Bethlehem & Abay, Kibrewossen, 2016. "Understanding farmers’ technology adoption decisions: Input complementarity and heterogeneity:," ESSP working papers 82, International Food Policy Research Institute (IFPRI).
    9. Tobias Müller & Stefan Boes, 2020. "Disability insurance benefits and labor supply decisions: evidence from a discontinuity in benefit awards," Empirical Economics, Springer, vol. 58(5), pages 2513-2544, May.
    10. Andreas Ziegler, 2010. "Individual Characteristics and Stated Preferences for Alternative Energy Sources and Propulsion Technologies in Vehicles: A Discrete Choice Analysis," CER-ETH Economics working paper series 10/125, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    11. Heiss, Florian & Winschel, Viktor, 2006. "Estimation with Numerical Integration on Sparse Grids," Discussion Papers in Economics 916, University of Munich, Department of Economics.
    12. Marco A. Palma & Dmitry V. Vedenov & David Bessler, 2020. "The order of variables, simulation noise, and accuracy of mixed logit estimates," Empirical Economics, Springer, vol. 58(5), pages 2049-2083, May.
    13. Andrea Morescalchi, 2016. "The Puzzle Of Job Search And Housing Tenure: A Reconciliation Of Theory And Empirical Evidence," Journal of Regional Science, Wiley Blackwell, vol. 56(2), pages 288-312, March.
    14. Islam, Mouyid, 2015. "Multi-Vehicle Crashes Involving Large Trucks: A Random Parameter Discrete Outcome Modeling Approach," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 54(1).
    15. Sándor, Zsolt & Train, Kenneth, 2004. "Quasi-random simulation of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 313-327, May.
    16. Train, Kenneth & Wilson, Wesley W., 2008. "Estimation on stated-preference experiments constructed from revealed-preference choices," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 191-203, March.
    17. Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
    18. Abay, Kibrom A., 2015. "Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariate binary probit models," Economics Letters, Elsevier, vol. 126(C), pages 51-56.
    19. Gould, Brian W. & Dong, Diansheng, 2000. "The Decision Of When To Buy A Frequently Purchased Good: A Multi-Period Probit Model," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(2), pages 1-17, December.
    20. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.

    More about this item

    Keywords

    Endogenous switching; duration analysis; probit; semi-nonparametric model; heterogeneity;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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

    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:hit:cisdps:597. 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: Digital Resources Section, Hitotsubashi University Library (email available below). General contact details of provider: https://edirc.repec.org/data/cihitjp.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.