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

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Author Info
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.

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Paper provided by The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham in its series Discussion Papers with number 2009-14.

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Date of creation: Jul 2009
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Handle: RePEc:cdx:dpaper:2009-14

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Related research
Keywords: Monte-Carlo; Simulation; Random Effects; Censored Regression Model; Public Goods; Heterogeneity; Endogeneity;

Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models
C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press. [Downloadable!]
  2. Nathaniel T Wilcox, 2006. "Theories of Learning in Games and Heterogeneity Bias," Econometrica, Econometric Society, vol. 74(5), pages 1271-1292, 09. [Downloadable!] (restricted)
  3. Rabe-Hesketh, Sophia & Skrondal, Anders & Pickles, Andrew, 2005. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects," Journal of Econometrics, Elsevier, vol. 128(2), pages 301-323, October. [Downloadable!] (restricted)
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This page was last updated on 2009-11-17.


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