IDEAS home Printed from https://ideas.repec.org/p/ags/aaea15/204963.html
   My bibliography  Save this paper

A New Estimator for Multivariate Binary Data

Author

Listed:
  • Fu, Shengfei
  • Shonkwiler, John Scott

Abstract

This study proposes a new estimator for multivariate binary response data. This study considers binary responses as being generated from a truncated multivariate discrete distribution. Specifically the discrete normal probability mass function, which has support on all integers, is extended to a multivariate form. Truncating this point probability mass function below zero and above one results the multivariate binary discrete normal distribution. This distribution has a number of attractive properties. Monte Carlo simulation and empirical applications are performed to show the properties of this new estimator; comparisons are made to the traditional multivariate probit model.

Suggested Citation

  • Fu, Shengfei & Shonkwiler, John Scott, 2015. "A New Estimator for Multivariate Binary Data," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204963, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:204963
    DOI: 10.22004/ag.econ.204963
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/204963/files/A%20New%20Estimator%20for%20Multivariate%20Binary%20Response%20Data.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.204963?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
    ---><---

    References listed on IDEAS

    as
    1. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    2. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    3. Vassilis A. Hajivassiliou & Daniel L. McFadden, 1998. "The Method of Simulated Scores for the Estimation of LDV Models," Econometrica, Econometric Society, vol. 66(4), pages 863-896, July.
    4. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    5. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    6. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    7. John Mullahy, 2011. "Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models, with Applications in Health Economics," NBER Working Papers 17588, National Bureau of Economic Research, Inc.
    8. Mullahy, John, 2011. "Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models," Working Papers 201135, Geary Institute, University College Dublin.
    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. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    2. Giovanni Bruno & Orietta Dessy, 2014. "Average partial effects in multivariate probit models with latent heterogeneity: Monte Carlo experiments and an application to immigrants' ethnic identity and economic performance," Italian Stata Users' Group Meetings 2014 10, Stata Users Group.
    3. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    4. Fernandez-Cornejo, Jorge & Wechsler, Seth James, 2012. "Fifteen Years Later: Examining the Adoption of Bt Corn Varieties by U.S. Farmers," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124257, Agricultural and Applied Economics Association.
    5. Agustí Segarra‐Blasco & Mercedes Teruel & Josep Tomàs‐Porres, 2024. "Circular economy and public policies: A dynamic analysis for European SMEs," Business Strategy and the Environment, Wiley Blackwell, vol. 33(4), pages 3532-3549, May.
    6. Richard Gates, 2006. "A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator," Stata Journal, StataCorp LP, vol. 6(2), pages 190-213, June.
    7. David Roodman, 2009. "Estimating Fully Observed Recursive Mixed-Process Models with cmp," Working Papers 168, Center for Global Development.
    8. Cristian Barra & Nazzareno Ruggiero, 2023. "Quality of Government and Types of Innovation—Empirical Evidence for Italian Manufacturing Firms," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(2), pages 1749-1789, June.
    9. David Roodman, 2011. "Fitting fully observed recursive mixed-process models with cmp," Stata Journal, StataCorp LP, vol. 11(2), pages 159-206, June.
    10. Vitaliy Roud & Valeriya Vlasova, 2016. "Firm-Level Evidence on the Cooperative Innovation Strategies in Russian Manufacturing," HSE Working papers WP BRP 63/STI/2016, National Research University Higher School of Economics.
    11. JongRoul Woo & HyungBin Moon & Jongsu Lee & Jinyong Jang, 2017. "Public attitudes toward the construction of new power plants in South Korea," Energy & Environment, , vol. 28(4), pages 499-517, June.
    12. Yasar, Mahmut, 2013. "Political Influence of Exporting and Import-Competing Firms: Evidence from Eastern European and Central Asian Countries," World Development, Elsevier, vol. 51(C), pages 154-168.
    13. Arimura, Toshi H. & Darnall, Nicole & Katayama, Hajime, 2011. "Is ISO 14001 a gateway to more advanced voluntary action? The case of green supply chain management," Journal of Environmental Economics and Management, Elsevier, vol. 61(2), pages 170-182, March.
    14. Yang Li & Asim Ansari, 2014. "A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models," Management Science, INFORMS, vol. 60(5), pages 1161-1179, May.
    15. Giulia Bettin & Riccardo Lucchetti, 2016. "Steady streams and sudden bursts: persistence patterns in remittance decisions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(1), pages 263-292, January.
    16. Hanna Hottenrott & Bettina Peters, 2012. "Innovative Capability and Financing Constraints for Innovation: More Money, More Innovation?," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1126-1142, November.
    17. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    18. Haojue Zhang & Yifu Sun & Changyu Meng, 2023. "Sustainable Urban Competitiveness from a Financial Development Perspective: An Empirical Study of China," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    19. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    20. Steve Bradley & Rob Crouchley, 2020. "The effects of test scores and truancy on youth unemployment and inactivity: a simultaneous equations approach," Empirical Economics, Springer, vol. 59(4), pages 1799-1831, October.

    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:ags:aaea15:204963. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.