Fixed and random effects in Classical and Bayesian regression
AbstractThis paper proposes a common and tractable framework for analyzing different definitions of fixed and random effects in a contant-slope variable-intercept model. It is shown that, regardless of whether effects (i) are treated as parameters or as an error term, (ii) are estimated in different stages of a hierarchical model, or whether (iii) correlation between effects and regressors is allowed, when the same information on effects is introduced into all estimation methods, the resulting slope estimator is also the same across methods. If different methods produce different results, it is ultimately because different information is being used for each methods.
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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 613.
Date of creation: Apr 2002
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Web page: http://www.econ.upf.edu/
Bayes; panel data; nuisance parameters; fixed effects; random effects;
Other versions of this item:
- Silvio R. Rendon, 2013. "Fixed and Random Effects in Classical and Bayesian Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 460-476, 06.
- Silvio Rendón, 2002. "Fixed And Random Effects In Classical And Bayesian Regression," Economics Working Papers we021503, Universidad Carlos III, Departamento de Economía.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
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