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The Limited Information Maximum Likelihood Approach to Dynamic Panel Structural Equations

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  • Kentaro Akashi

    (The Institute of Statistical Mathematics)

  • Naoto Kunitomo

    (Faculty of Economics, University of Tokyo)

Abstract

We develop the panel limited information maximum likelihood (PLIML) approach for estimating dynamic panel structural equation models. When there are dynamic effects and endogenous variables with individual effects at the same time, the PLIML estimation method for the filtered data does give not only a consistent estimator, but also it has the asymptotic normality and often attains the asymptotic bound when the number of orthogonal conditions is large. Our formulation includes Alvarez and Arellano (2003), Blundell and Bond (2000) and other linear dynamic panel models as special cases. We also compare the PLIML and Panel GMM methods and propose an improvement of PLIML for heteroscedastic disturbances among many indivisuals.

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File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2010/2010cf708.pdf
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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-708.

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Length: 52pages
Date of creation: Jan 2010
Date of revision:
Handle: RePEc:tky:fseres:2010cf708

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References

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  1. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
  2. Kazuhiko Hayakawa, 2007. "A Simple Efficient Instrumental Variable Estimator in Panel AR(p) Models," Hi-Stat Discussion Paper Series d07-213, Institute of Economic Research, Hitotsubashi University.
  3. Kazuhiko Hayakawa, 2006. "Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present," Hi-Stat Discussion Paper Series d05-130, Institute of Economic Research, Hitotsubashi University.
  4. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On Finite Sample Properties of Alternative Estimators of Coefficients in a Structural Equation with Many Instruments," CIRJE F-Series CIRJE-F-577, CIRJE, Faculty of Economics, University of Tokyo.
  5. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2006. "A New Light from Old Wisdoms : Alternative Estimation Methods of Simultaneous Equations with Possibly Many Instruments," CIRJE F-Series CIRJE-F-399, CIRJE, Faculty of Economics, University of Tokyo.
  6. Akashi, Kentaro & Kunitomo, Naoto, 2012. "Some properties of the LIML estimator in a dynamic panel structural equation," Journal of Econometrics, Elsevier, vol. 166(2), pages 167-183.
  7. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On the Asymptotic Optimality of the LIML Estimator with Possibly Many Instruments," CIRJE F-Series CIRJE-F-542, CIRJE, Faculty of Economics, University of Tokyo.
  8. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
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Cited by:
  1. Naoto Kunitomo & Kentaro Akashi, 2010. "An Aysmptotically Optimal Modification of the Panel LIML Estimation for Individual Heteroscedasticity," CIRJE F-Series CIRJE-F-780, CIRJE, Faculty of Economics, University of Tokyo.
  2. Huang, Yongfu & Quibria, M. G., 2013. "The global partnership for inclusive growth," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
  3. Kentaro Akashi & Naoto Kunitomo, 2010. "Some Properties of the LIML Estimator in a Dynamic Panel Structural Equation," CIRJE F-Series CIRJE-F-707, CIRJE, Faculty of Economics, University of Tokyo.

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