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Conditions initiales et estimation efficace dans les modéles dynamiques sur données de panel : une application au comportement d'investissement des entreprises

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  • Richard Blundell
  • Richard J. Smith

Abstract

This paper provides a methodology for efficient estimation of dynamic panel data models under different assumptions concerning initial conditions and variance assumptions. In the analysis of panel data models that may involve lagged dependent variables, concerns about the initial conditions reflect the fact that most panel data sets have a relatively small time series dimension. Our model specification is a linear first-order dynamic model in which there are assumed to exist an identifying subset of strictly exogenous regressors. Under all but the most trivial assumption concerning the initial conditions the standard GLS procedure is inconsistent. We argue that our approach provides a natural procedure for testing assumptions concerning the process underlying the initial conditions. The methodology is applied to the estimation of a dynamic model of investment behaviour using panel data.

Suggested Citation

  • Richard Blundell & Richard J. Smith, 1991. "Conditions initiales et estimation efficace dans les modéles dynamiques sur données de panel : une application au comportement d'investissement des entreprises," Annals of Economics and Statistics, GENES, issue 20-21, pages 109-123.
    • Handle: RePEc:adr:anecst:y:1991:i:20-21:p:109-123
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    Cited by:

    1. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    2. De Blander, Rembert, 2020. "Iterative estimation correcting for error auto-correlation in short panels, applied to lagged dependent variable models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 3-29.
    3. Barbosa, José Diogo & Moreira, Marcelo J., 2021. "Likelihood inference and the role of initial conditions for the dynamic panel data model," Journal of Econometrics, Elsevier, vol. 221(1), pages 160-179.
    4. Harris, Richard D. F. & Tzavalis, Elias, 1999. "Inference for unit roots in dynamic panels where the time dimension is fixed," Journal of Econometrics, Elsevier, vol. 91(2), pages 201-226, August.
    5. N. R. Ramírez-Rondán, 2020. "Maximum likelihood estimation of dynamic panel threshold models," Econometric Reviews, Taylor & Francis Journals, vol. 39(3), pages 260-276, March.
    6. Arturo Lamadrid-Contreras & N.R. Ramírez-Rondán, 2018. "Panel Models with Two Threshold Variables: The Case of Financial Constraints," Working Papers 128, Peruvian Economic Association.
    7. El Aynaoui, Karim & Ibourk, Aomar, 2014. "Les déterminants des exportations du Maroc : une investigation empirique sur données de panel [The determinants of Morocco's exports: An empirical investigation using panel data]," MPRA Paper 63021, University Library of Munich, Germany, revised 2014.
    8. Mourad Zmami & Ousama Ben-Salha, 2015. "The adjustment of plant-level investment to exchange rate fluctuations in Tunisia: do the size and the ownership structure matter?," Economics Bulletin, AccessEcon, vol. 35(4), pages 2487-2505.
    9. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.

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