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An Alternative Derivation Of Mundlak'S Fixed Effects Results Using System Estimation

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  • Baltagi, Badi H.

Abstract

Mundlak (1978, Econometrica 46, 69–85) showed that the fixed effects estimator can be obtained as generalized least squares (GLS) for a panel regression model where the individual effects are random but are all hopelessly correlated with the regressors. This result was obtained by partitioned inversion after substituting the reduced form expression for the individual effects as a function of the means of all the regressors. This note shows that Mundlak's result can be obtained using system estimation without using partitioned inversion. System estimation has proved useful for deriving two-stage least squares (2SLS) and three-stage least squares (3SLS) counterparts for the random effects panel models by Baltagi (1981, Journal of Econometrics 17, 189–200). It also has been used for obtaining an alternative derivation of the Hausman tests that is robust to heteroskedasticity of unknown form (see Arellano, 1993, Journal of Econometrics 59, 87–97) and more recently, for obtaining generalized method of moments (GMM) estimators for dynamic panel models (see Arellano and Bover, 1995, Journal of Econometrics 68, 29–51; and Blundell and Bond, 1998, Journal of Econometrics 87, 115–143, to mention a few). We also show that a necessary and sufficient condition for ordinary least squares (OLS) to be equivalent to GLS is satisfied for this model.

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  • 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.
    • Handle: RePEc:cup:etheor:v:22:y:2006:i:06:p:1191-1194_06
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    Cited by:

    1. Badi H. Baltagi, 2023. "The Mundlak spatial estimator," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-8, December.
    2. Pengfei Sheng & Tingting Yang & Tengfei Zhang, 2021. "The Unmet Medical Demand among China’s Urban Residents," IJERPH, MDPI, vol. 18(21), pages 1-13, November.
    3. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    4. Olatunji Abdul Shobande, 2021. "Decomposing the Persistent and Transitory Effect of Information and Communication Technology on Environmental Impacts Assessment in Africa: Evidence from Mundlak Specification," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    5. Yang, Yimin, 2022. "A correlated random effects approach to the estimation of models with multiple fixed effects," Economics Letters, Elsevier, vol. 213(C).
    6. Bělín, Matěj, 2020. "Time-invariant regressors under fixed effects: Simple identification via a proxy variable," Economics Letters, Elsevier, vol. 186(C).
    7. Bache, Stefan Holst Milton & Kristensen, Troels, 2013. "A simple but efficient approach to the analysis of multilevel data," DaCHE discussion papers 2013:6, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    8. Pengfei Sheng & Yuechi Zhang, 2019. "The effect of pollution on the inequality in health care expenditure: Evidence from China," Energy & Environment, , vol. 30(8), pages 1380-1395, December.
    9. Shi, Miaoying & Yin, Runsheng & Lv, Hongdi, 2017. "An empirical analysis of the driving forces of forest cover change in northeast China," Forest Policy and Economics, Elsevier, vol. 78(C), pages 78-87.
    10. Shobande, Olatunji A., 2023. "Rethinking social change: Does the permanent and transitory effects of electricity and solid fuel use predict health outcome in Africa?," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

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