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The Mundlak spatial estimator

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  • Badi H. Baltagi

    (Syracuse University)

Abstract

The spatial Mundlak model first considered by Debarsy (2012) is an alternative to fixed effects and random effects estimation for spatial panel data models. Mundlak modelled the correlated random individual effects as a linear combination of the averaged regressors over time plus a random time-invariant error. This paper shows that if spatial correlation is present whether spatial lag or spatial error or both, the standard Mundlak result in panel data does not hold and random effects does not reduce to its fixed effects counterpart. However, using maximum likelihood one can still estimate these spatial Mundlak models and test the correlated random effects specification of Mundlak using Likelihood ratio tests as demonstrated by Debarsy for the Mundlak spatial Durbin model.

Suggested Citation

  • Badi H. Baltagi, 2023. "The Mundlak spatial estimator," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-8, December.
    • Handle: RePEc:spr:jospat:v:4:y:2023:i:1:d:10.1007_s43071-023-00037-y
    DOI: 10.1007/s43071-023-00037-y
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    References listed on IDEAS

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    1. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    2. Baltagi, Badi H., 2006. "An Alternative Derivation Of Mundlak'S Fixed Effects Results Using System Estimation," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1191-1194, December.
    3. Badi H. Baltagi, 2021. "Econometric Analysis of Panel Data," Springer Texts in Business and Economics, Springer, edition 6, number 978-3-030-53953-5, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Mundlak regression; Panel data; Fixed and random effects; Spatial error model; Spatial Durbin model;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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