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Dynamic Linear Models

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The Econometrics of Panel Data

Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 33))

Abstract

One of the main advantages of panel data is that it allows one to study the dynamics of economic behaviour at an individual level. Unfortunately, when dynamic models are estimated using time series of cross sections data, the usual least squares methods (such as those presented in Chapters 3 and 4) do not lead to consistent estimates for the parameters of the two most commonly used models for panel data (i.e., fixed effects and error components models). This inconsistency results from the fact that the disturbance terms are serially correlated in these models, which causes the lagged endogenous variable to be correlated with those disturbances. As in the time series context, we do not have analytical results about the small sample properties of the various estimators of these models. The only available results come from Monte-Carlo simulation studies (see Nerlove [1967], [1971]). Hence, one must rely on the asymptotic properties of these methods and assume that the size of the sample grows to infinity.

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Authors
  1. Patrick Sevestre
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  2. Alain Trognon
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Editor information

Editors and Affiliations

  1. Monash University, Melbourne and Budapest University of Economics, Australia

    László Mátyás

  2. ERUDITE, Université de Paris-Val-de-Marne, France

    Patrick Sevestre

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© 1996 Kluwer Academic Publishers

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Sevestre, P., Trognon, A. (1996). Dynamic Linear Models. In: Mátyás, L., Sevestre, P. (eds) The Econometrics of Panel Data. Advanced Studies in Theoretical and Applied Econometrics, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0137-7_7

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  • DOI: https://doi.org/10.1007/978-94-009-0137-7_7

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-7923-3787-4

  • Online ISBN: 978-94-009-0137-7

  • eBook Packages: Springer Book Archive

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