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dc.contributor.authorPoen, Evaen_US
dc.date.accessioned2011-09-26T13:24:12Z-
dc.date.available2011-09-26T13:24:12Z-
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10419/49679-
dc.description.abstractWe 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.en_US
dc.language.isoengen_US
dc.publisherCEDEX Nottinghamen_US
dc.relation.ispartofseriesCeDEx discussion paper series 2009-14en_US
dc.subject.jelC15en_US
dc.subject.jelC24en_US
dc.subject.jelC92en_US
dc.subject.ddc330en_US
dc.subject.keywordMonte-Carloen_US
dc.subject.keywordsimulationen_US
dc.subject.keywordrandom effectsen_US
dc.subject.keywordcensored regression modelen_US
dc.subject.keywordpublic goodsen_US
dc.subject.keywordheterogeneityen_US
dc.subject.keywordendogeneityen_US
dc.subject.stwÖffentliches Guten_US
dc.subject.stwRegressionen_US
dc.subject.stwSpieltheorieen_US
dc.subject.stwTobit-Modellen_US
dc.titleThe tobit model with feedback and random effects: A Monte-Carlo studyen_US
dc.typeWorking Paperen_US
dc.identifier.ppn604464290en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:CeDEx Discussion Paper Series, The University of Nottingham

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