[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v86y2015icp81-96.html
   My bibliography  Save this article

Bayesian estimation of a discrete response model with double rules of sample selection

Author

Listed:
  • Zhang, Rong
  • Inder, Brett A.
  • Zhang, Xibin
Abstract
A Bayesian sampling algorithm for parameter estimation in a discrete-response model is presented, where the dependent variables contain two layers of binary choices and one ordered response. The investigation is motivated by an empirical study using such a double-selection rule for three labour-market outcomes, namely labour-force participation, employment and occupational skill level. It is of particular interest to measure the marginal effects of some mental health factors on these labour-market outcomes. The contribution is to present a sampling algorithm, which is a hybrid of Gibbs and Metropolis–Hastings algorithms. In Monte Carlo simulations, numerical maximization of likelihood fails to converge for more than half of the simulated samples. The proposed Bayesian method represents a substantial improvement: it converges in every sample, and performs with similar or better precision than maximum likelihood. The proposed sampling algorithm is applied to the double-selection model of labour-force participation, employment and occupational skill level, where marginal effects of explanatory variables, in particular the mental health factors, on the three labour-force outcomes are assessed through 95% Bayesian credible intervals. The proposed sampling algorithm can easily be modified for other multivariate nonlinear models that involve selectivity and are difficult to estimate by other means.

Suggested Citation

  • Zhang, Rong & Inder, Brett A. & Zhang, Xibin, 2015. "Bayesian estimation of a discrete response model with double rules of sample selection," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 81-96.
    • Handle: RePEc:eee:csdana:v:86:y:2015:i:c:p:81-96
    DOI: 10.1016/j.csda.2014.12.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315000134
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.12.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Richard Blundell & John Ham & Costas Meghir, 1989. "Unemployment and Female Labour Supply," Palgrave Macmillan Books, in: Joan Muysken & Chris Neubourg (ed.), Unemployment in Europe, chapter 1, pages 9-36, Palgrave Macmillan.
    2. Li Mingliang & Tobias Justin L, 2006. "Bayesian Analysis of Structural Effects in an Ordered Equation System," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(4), pages 1-24, December.
    3. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    4. Murray D. Smith, 2003. "Modelling sample selection using Archimedean copulas," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 99-123, June.
    5. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    6. Zhang, Xibin & Brooks, Robert D. & King, Maxwell L., 2009. "A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation," Journal of Econometrics, Elsevier, vol. 153(1), pages 21-32, November.
    7. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    8. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, September.
    9. 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.
    10. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    11. Greene, William & Harris, Mark N. & Hollingsworth, Bruce & Maitra, Pushkar, 2014. "A latent class model for obesity," Economics Letters, Elsevier, vol. 123(1), pages 1-5.
    12. van Hasselt, Martijn, 2011. "Bayesian inference in a sample selection model," Journal of Econometrics, Elsevier, vol. 165(2), pages 221-232.
    13. Madhu Mohanty, 2001. "Testing for the specification of the wage equation: double selection approach or single selection approach," Applied Economics Letters, Taylor & Francis Journals, vol. 8(8), pages 525-529.
    14. William Greene, 2006. "A General Approach to Incorporating Selectivity in a Model," Working Papers 06-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dawood Ashraf & Mohsin Khawaja & M. Ishaq Bhatti, 2022. "Raising capital amid economic policy uncertainty: an empirical investigation," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-32, December.
    2. Wojtyś, Małgorzata & Marra, Giampiero & Radice, Rosalba, 2018. "Copula based generalized additive models for location, scale and shape with non-random sample selection," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 1-14.
    3. Zeng-Hua Lu & Alec Zuo, 2017. "Child disability, welfare payments, marital status and mothers’ labor supply: Evidence from Australia," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1339769-133, January.

    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. Rong Zhang & Brett A. Inder & Xibin Zhang, 2013. "Bayesian estimation of a discrete response model with double rules of sample selection," Monash Econometrics and Business Statistics Working Papers 24/13, Monash University, Department of Econometrics and Business Statistics.
    2. Anastasios Panagiotelis & Michael S. Smith & Peter J. Danaher, 2014. "From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence, and Visit Behavior," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 14-29, January.
    3. Rong Zhang & Brett A. Inder & Xibin Zhang, 2012. "Parameter estimation for a discrete-response model with double rules of sample selection: A Bayesian approach," Monash Econometrics and Business Statistics Working Papers 5/12, Monash University, Department of Econometrics and Business Statistics.
    4. Wiemann, Paul F.V. & Klein, Nadja & Kneib, Thomas, 2022. "Correcting for sample selection bias in Bayesian distributional regression models," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    5. Martijn van Hasselt, 2005. "Bayesian Sampling Algorithms for the Sample Selection and Two-Part Models," Computing in Economics and Finance 2005 241, Society for Computational Economics.
    6. Marra, Giampiero & Radice, Rosalba, 2013. "Estimation of a regression spline sample selection model," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 158-173.
    7. Didier Nibbering, 2019. "A High-dimensional Multinomial Choice Model," Monash Econometrics and Business Statistics Working Papers 19/19, Monash University, Department of Econometrics and Business Statistics.
    8. van Hasselt, Martijn, 2011. "Bayesian inference in a sample selection model," Journal of Econometrics, Elsevier, vol. 165(2), pages 221-232.
    9. Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.
    10. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    11. Stephan Wachtel & Thomas Otter, 2013. "Successive Sample Selection and Its Relevance for Management Decisions," Marketing Science, INFORMS, vol. 32(1), pages 170-185, September.
    12. John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo.
    13. Begin, Rosemarie & Tamini, Lota D. & Doyon, Maurice, 2014. "L'effet du travail hors-ferme sur l'efficacité technique des fermes laitières québécoises: un modèle intégrant les biais de sélection sur les observables et inobservables," Working Papers 187233, University of Laval, Center for Research on the Economics of the Environment, Agri-food, Transports and Energy (CREATE).
    14. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.
    15. Sizhong Sun, 2023. "Firm heterogeneity, worker training and labor productivity: the role of endogenous self-selection," Journal of Productivity Analysis, Springer, vol. 59(2), pages 121-133, April.
    16. Hasebe, Takuya & Vijverberg, Wim P., 2012. "A Flexible Sample Selection Model: A GTL-Copula Approach," IZA Discussion Papers 7003, Institute of Labor Economics (IZA).
    17. Seonho Shin, 2022. "To work or not? Wages or subsidies?: Copula-based evidence of subsidized refugees’ negative selection into employment," Empirical Economics, Springer, vol. 63(4), pages 2209-2252, October.
    18. Dogan, Osman & Taspinar, Suleyman, 2016. "Bayesian Inference in Spatial Sample Selection Models," MPRA Paper 82829, University Library of Munich, Germany.
    19. Lalive, Rafael & Schmutzler, Armin, 2011. "Auctions vs Negotiations in Public Procurement: Which Works Better?," CEPR Discussion Papers 8538, C.E.P.R. Discussion Papers.
    20. David Mayston & Juan Yang, 2012. "Education, Risk and Efficiency in Human Capital Investment," Discussion Papers 12/15, Department of Economics, University of York.

    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:eee:csdana:v:86:y:2015:i:c:p:81-96. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.