IDEAS home Printed from https://ideas.repec.org/p/not/notcdx/2009-14.html
   My bibliography  Save this paper

The Tobit model with feedback and random effects: A Monte-Carlo study

Author

Listed:
  • Eva Poen

    (CeDEx, School of Economics, University of Nottingham)

Abstract

We study a random effects censored regression model in the context of repeated games. Introducing a feedback variable into the model leads to violation of the strict exogeneity assumption, thus rendering the random effects estimator inconsistent. Using the example of contributions to a public good, we investigate the size of this bias in a Monte-Carlo study. We find that the magnitude of the bias is around one per cent when initial values and individual effects are correlated. The rate of censoring, as well as the size of the groups in which subjects interact, both have an effect on the magnitude of the bias. The coefficients of strictly exogenous, continuous regressors remain unaffected by the endogeneity bias. The size of the endogeneity bias in our model is very small compared to the size of the heterogeneity bias, which occurs when individual heterogeneity is not accounted for in estimation of nonlinear models.

Suggested Citation

  • Eva Poen, 2009. "The Tobit model with feedback and random effects: A Monte-Carlo study," Discussion Papers 2009-14, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
  • Handle: RePEc:not:notcdx:2009-14
    as

    Download full text from publisher

    File URL: https://www.nottingham.ac.uk/cedex/documents/papers/2009-14.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press.
    2. Nathaniel T Wilcox, 2006. "Theories of Learning in Games and Heterogeneity Bias," Econometrica, Econometric Society, vol. 74(5), pages 1271-1292, September.
    3. Sophia Rabe-Hesketh & Anders Skrondal, 2007. "Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 123-140, June.
    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. Merrett, Danielle, 2012. "Estimation of Public Goods Game Data," Working Papers 2012-09, University of Sydney, School of Economics.
    2. Ramón Cobo-Reyes & Gabriel Katz & Thomas Markussen & Simone Meraglia, 2022. "Voting on sanctioning institutions in open and closed communities: experimental evidence," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 58(3), pages 619-677, April.
    3. Cobo-Reyes, Ramón & Katz, Gabriel & Meraglia, Simone, 2019. "Endogenous sanctioning institutions and migration patterns: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 575-606.

    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. Eva Poen, 2009. "The Tobit model with feedback and random effects: A Monte-Carlo study," Discussion Papers 2009-14, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    2. Terracol, Antoine & Vaksmann, Jonathan, 2009. "Dumbing down rational players: Learning and teaching in an experimental game," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 54-71, May.
    3. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
    4. Hanel, Barbara & Riphahn, Regina T., 2012. "The timing of retirement — New evidence from Swiss female workers," Labour Economics, Elsevier, vol. 19(5), pages 718-728.
    5. Li Yu & Peter F. Orazem, 2014. "O-Ring production on U.S. hog farms: joint choices of farm size, technology, and compensation," Agricultural Economics, International Association of Agricultural Economists, vol. 45(4), pages 431-442, July.
    6. Andrew K. Rose & Mark M. Spiegel, 2010. "Cross‐Country Causes And Consequences Of The 2008 Crisis: International Linkages And American Exposure," Pacific Economic Review, Wiley Blackwell, vol. 15(3), pages 340-363, August.
    7. Cason, Timothy N. & Friedman, Daniel & Hopkins, Ed, 2010. "Testing the TASP: An experimental investigation of learning in games with unstable equilibria," Journal of Economic Theory, Elsevier, vol. 145(6), pages 2309-2331, November.
    8. Mohlin, Erik & Östling, Robert & Wang, Joseph Tao-yi, 2020. "Learning by similarity-weighted imitation in winner-takes-all games," Games and Economic Behavior, Elsevier, vol. 120(C), pages 225-245.
    9. Fudenberg, Drew & Takahashi, Satoru, 2011. "Heterogeneous beliefs and local information in stochastic fictitious play," Games and Economic Behavior, Elsevier, vol. 71(1), pages 100-120, January.
    10. Salvatori, Andrea, 2010. "Labour contract regulations and workers' wellbeing: International longitudinal evidence," Labour Economics, Elsevier, vol. 17(4), pages 667-678, August.
    11. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    12. Hayo, Bernd & Vollan, Björn, 2012. "Group interaction, heterogeneity, rules, and co-operative behaviour: Evidence from a common-pool resource experiment in South Africa and Namibia," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 9-28.
    13. Hu, Yingyao & Kayaba, Yutaka & Shum, Matthew, 2013. "Nonparametric learning rules from bandit experiments: The eyes have it!," Games and Economic Behavior, Elsevier, vol. 81(C), pages 215-231.
    14. Daniele Vignoli & Gustavo Santis, 2010. "Individual and Contextual Correlates of Economic Difficulties in Old Age in Europe," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 29(4), pages 481-501, August.
    15. Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
    16. Sebastian Königs, 2013. "The Dynamics of Social Assistance Benefit Receipt in Germany: State Dependence Before and After the Hartz Reforms," OECD Social, Employment and Migration Working Papers 136, OECD Publishing.
    17. Margaret Levi & Audrey Sacks, 2009. "Legitimating beliefs: Sources and indicators," Regulation & Governance, John Wiley & Sons, vol. 3(4), pages 311-333, December.
    18. Barbara Hanel & Regina Riphahn, 2006. "Financial Incentives and the Timing of Retirement: Evidence from Switzerland," Working Papers 009, Bavarian Graduate Program in Economics (BGPE).
    19. Barbara Hanel, 2010. "Disability Pensions and Labor Supply," Working Papers 086, Bavarian Graduate Program in Economics (BGPE).
    20. Bethany Cooper & Michael Burton & Lin Crase, 2019. "Willingness to Pay to Avoid Water Restrictions in Australia Under a Changing Climate," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(3), pages 823-847, March.

    More about this item

    Keywords

    Monte-Carlo; Simulation; Random Effects; Censored Regression Model; Public Goods; Heterogeneity; Endogeneity;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

    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:not:notcdx:2009-14. 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: Jose V Guinot Saporta (email available below). General contact details of provider: https://edirc.repec.org/data/cdnotuk.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.