IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v64y2013icp192-208.html
   My bibliography  Save this article

A Bayesian multivariate probit for ordinal data with semiparametric random-effects

Author

Listed:
  • Kim, Jung Seek
  • Ratchford, Brian T.

Abstract

A heterogeneous thresholds probit for ordered ratings is developed to remove conditional independence among responses and incorporate respondent traits. We propose a semiparametric approach to relaxing normality of random-effects in the probit model that account for differences in response style. Simulation studies provide evidence of the ability for the proposed semiparametric model to better recover an underlying distribution of respondent effects than the parametric one with a normal hierarchical prior. The application to ratings on the value of information sources for automobiles demonstrates significant correlations among responses and irregularity in the shape of unobserved heterogeneity.

Suggested Citation

  • Kim, Jung Seek & Ratchford, Brian T., 2013. "A Bayesian multivariate probit for ordinal data with semiparametric random-effects," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 192-208.
    • Handle: RePEc:eee:csdana:v:64:y:2013:i:c:p:192-208
    DOI: 10.1016/j.csda.2013.03.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947313001023
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2013.03.004?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. Rossi P. E & Gilula Z. & Allenby G. M, 2001. "Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 20-31, March.
    2. Saskia Litière & Ariel Alonso & Geert Molenberghs, 2007. "Type I and Type II Error Under Random-Effects Misspecification in Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1038-1044, December.
    3. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    4. Forrest Young, 1981. "Quantitative analysis of qualitative data," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 357-388, December.
    5. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    6. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    7. Timothy Johnson, 2003. "On the use of heterogeneous thresholds ordinal regression models to account for individual differences in response style," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 563-583, December.
    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. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    2. Rainer Hirk & Kurt Hornik & Laura Vana, 2019. "Multivariate ordinal regression models: an analysis of corporate credit ratings," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 507-539, September.
    3. Ryo Kato & Takahiro Hoshino, 2018. "Semiparametric Bayes Multiple Imputation for Regression Models with Missing Mixed Continuous-Discrete Covariates," Discussion Paper Series DP2018-15, Research Institute for Economics & Business Administration, Kobe University.

    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. Timothy Gilbride & Sha Yang & Greg Allenby, 2005. "Modeling Simultaneity in Survey Data," Quantitative Marketing and Economics (QME), Springer, vol. 3(4), pages 311-335, December.
    2. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    3. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
    4. Kajal Lahiri & Chuanming Gao & Bernard Wixon, 2020. "Value of Sample Separation Information in a Sequential Probit Model," Arthaniti: Journal of Economic Theory and Practice, , vol. 19(2), pages 151-176, December.
    5. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    6. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    7. Dong, Diansheng & Stewart, Hayden & McLaughlin, Patrick W., 2017. "A New Approach for Modeling Household Food Demand with Panel Data: The Case of Cold Cereals," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258195, Agricultural and Applied Economics Association.
    8. Maruyama, Shiko, 2014. "Estimation of finite sequential games," Journal of Econometrics, Elsevier, vol. 178(2), pages 716-726.
    9. Falk Bräuning & Siem Jan Koopman, 2016. "The dynamic factor network model with an application to global credit risk," Working Papers 16-13, Federal Reserve Bank of Boston.
    10. Inkmann, Joachim, 1997. "Circumventing multiple integration: A comparison of GMM and SML estimators for the panel probit model," Discussion Papers, Series II 339, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    11. Kim, Vitally, 2010. "Level of alcohol consumption and worker’s labor market position. Multivariate probit model approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 18(2), pages 53-77.
    12. Emmanuel Duguet & Claire Lelarge, 2012. "Does Patenting Increase the Private Incentives to Innovate? A Microeconometric Analysis," Annals of Economics and Statistics, GENES, issue 107-108, pages 201-238.
    13. Ziegler, Andreas, 2001. "Simulated z-tests in multinomial probit models," ZEW Discussion Papers 01-53, ZEW - Leibniz Centre for European Economic Research.
    14. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    15. Laurent Davezies & Xavier D'Haultfoeuille & Denis Fougère, 2009. "Identification of peer effects using group size variation," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 397-413, November.
    16. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
    17. 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.
    18. Bryan S. Graham & Andrin Pelican, 2023. "Scenario sampling for large supermodular games," CeMMAP working papers 15/23, Institute for Fiscal Studies.
    19. Rennings, Klaus & Ziegler, Andreas & Zwick, Thomas, 2001. "Employment changes in environmentally innovative firms," ZEW Discussion Papers 01-46, ZEW - Leibniz Centre for European Economic Research.
    20. 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).

    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:64:y:2013:i:c:p:192-208. 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.