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Fixed and random effects in Classical and Bayesian regression

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  • Silvio Rendón

Abstract

This 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.

Suggested Citation

  • Silvio Rendón, 2002. "Fixed and random effects in Classical and Bayesian regression," Economics Working Papers 613, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:613
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    References listed on IDEAS

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    1. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
    2. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    3. Nerlove,Marc, 2005. "Essays in Panel Data Econometrics," Cambridge Books, Cambridge University Press, number 9780521022460.
    4. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-358, March.
    5. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    6. Wallace, T D & Hussain, Ashiq, 1969. "The Use of Error Components Models in Combining Cross Section with Time Series Data," Econometrica, Econometric Society, vol. 37(1), pages 55-72, January.
    7. Boozer, Michael A., 1997. "Econometric Analysis of Panel Data Badi H. Baltagi Wiley, 1995," Econometric Theory, Cambridge University Press, vol. 13(05), pages 747-754, October.
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    Cited by:

    1. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2018. "Robust linear static panel data models using ε-contamination," Journal of Econometrics, Elsevier, vol. 202(1), pages 108-123.
    2. Ignacio Abásolo & Miguel Negrín & Jaime Pinilla, 2014. "Utilización y tiempos de espera: dos vertientes inseparables del análisis de la equidad en el acceso al sistema sanitario público," Hacienda Pública Española, IEF, vol. 208(1), pages 11-38, March.
    3. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    4. Han, Xiaoyi & Hsieh, Chih-Sheng & Lee, Lung-fei, 2017. "Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 97-120.
    5. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2014. "Robust linear static panel data models using epsilon-contamination," MPRA Paper 59896, University Library of Munich, Germany.
    6. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2014. "Robust Linear Static Panel Data Models Using ?-Contamination," IZA Discussion Papers 8661, Institute for the Study of Labor (IZA).
    7. Hsieh, Chih-Sheng & Lee, Lung fei, 2017. "Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution," MPRA Paper 60726, University Library of Munich, Germany.

    More about this item

    Keywords

    Bayes; panel data; nuisance parameters; fixed effects; random effects;

    JEL classification:

    • 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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