IDEAS home Printed from https://ideas.repec.org/p/ste/nystbu/03-19.html
   My bibliography  Save this paper

A Interpreting Estimated Parameters and Measuring Individual Heterogeneity in Random Coefficient Models

Author

Listed:
  • William Greene

Abstract

No abstract is available for this item.

Suggested Citation

  • William Greene, 2003. "A Interpreting Estimated Parameters and Measuring Individual Heterogeneity in Random Coefficient Models," Working Papers 03-19, New York University, Leonard N. Stern School of Business, Department of Economics.
    • Handle: RePEc:ste:nystbu:03-19
    as

    Download full text from publisher

    File URL: http://w4.stern.nyu.edu/economics/docs/workingpapers/2003/03-19Greene.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kamakura, Wagner A & Wedel, Michel, 2004. "An Empirical Bayes Procedure for Improving Individual-Level Estimates and Predictions from Finite Mixtures of Multinomial Logit Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 121-125, January.
    2. William Greene, 2004. "Convenient estimators for the panel probit model: Further results," Empirical Economics, Springer, vol. 29(1), pages 21-47, January.
    3. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    5. Bertschek, Irene & Lechner, Michael, 1998. "Convenient estimators for the panel probit model," Journal of Econometrics, Elsevier, vol. 87(2), pages 329-371, September.
    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. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    2. Belderbos, Rene & Carree, Martin & Diederen, Bert & Lokshin, Boris & Veugelers, Reinhilde, 2004. "Heterogeneity in R&D cooperation strategies," International Journal of Industrial Organization, Elsevier, vol. 22(8-9), pages 1237-1263, November.
    3. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.
    4. Martin Burda & Roman Liesenfeld & Jean-Francois Richard, 2008. "Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors," Working Papers tecipa-321, University of Toronto, Department of Economics.
    5. Jürgen Bierbaumer-Polly & Werner Hölzl, 2016. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    6. 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".
    7. Hammon, Angelina & Zinn, Sabine, 2020. "Multiple imputation of binary multilevel missing not at random data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 69(3), pages 547-564.
    8. Hübler, Olaf, 2005. "Panel Data Econometrics: Modelling and Estimation," Hannover Economic Papers (HEP) dp-319, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    9. Breitung, Jörg & Lechner, Michael, 1998. "Alternative GMM methods for nonlinear panel data models," SFB 373 Discussion Papers 1998,81, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    10. William Greene, 2007. "Discrete Choice Modeling," Working Papers 07-6, New York University, Leonard N. Stern School of Business, Department of Economics.
    11. Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Schim van der Loeff, 2007. "The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models," CIRANO Working Papers 2007s-06, CIRANO.
    12. Moura, Guilherme V. & Richard, Jean-François & Liesenfeld, Roman, 2007. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Economics Working Papers 2007-11, Christian-Albrechts-University of Kiel, Department of Economics.
    13. Yanhong H. Jin & James W. Mjelde & Kerry K. Litzenberg, 2014. "Economic analysis of job-related attributes in undergraduate students' initial job selection," Education Economics, Taylor & Francis Journals, vol. 22(3), pages 305-327, June.
    14. Kevin F. Hallock & Craig Olson, 2006. "The Value of Stock Options to Non-Executive Employees," NBER Working Papers 11950, National Bureau of Economic Research, Inc.
    15. 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.
    16. Inkmann, Joachim, 2000. "Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators," Journal of Econometrics, Elsevier, vol. 97(2), pages 227-259, August.
    17. Henry W. Chappell & William Greene & Mark N. Harris & Christopher Spencer, 2022. "Uncertainty and the Bank of England's MPC," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(4), pages 825-858, June.
    18. Aßmann, Christian & Boysen-Hogrefe, Jens, 2009. "A bayesian approach to model-based clustering for panel probit models," Economics Working Papers 2009-03, Christian-Albrechts-University of Kiel, Department of Economics.
    19. Novikova, Anastasija & Rocchi, Lucia & Vitunskienė, Vlada, 2017. "Assessing the benefit of the agroecosystem services: Lithuanian preferences using a latent class approach," Land Use Policy, Elsevier, vol. 68(C), pages 277-286.
    20. Olaf Hübler, 2006. "Multilevel and nonlinear panel data models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 121-136, March.

    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

    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:ste:nystbu:03-19. 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: Amanda Murphy (email available below). General contact details of provider: https://edirc.repec.org/data/ednyuus.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.