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The Conditional Limited Information Maximum Likelihood Approach to Dynamic Panel Structural Equations

  • Naoto Kunitomo

    (Faculty of Economics, University of Tokyo)

  • Kentaro Akashi

    (Graduate School of Economics, University of Tokyo)

We propose the conditional limited information maximum likelihood (CLIML) approach for estimating dynamic panel structural equation models. When there are dynamic effects and endogenous variables with individual effects at the same time, the CLIML estimation method for the doubly-filtered data does give not only a consistent estimation, but also it attains the asymptotic efficiency when the number of orthogonal condition is large. Our formulation includes Alvarez and Arellano (2003), Blundell and Bond (2000) and other linear dynamic panel models as special cases.

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File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2007/2007cf503.pdf
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Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-503.

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Length: 33 pages
Date of creation: Jul 2007
Date of revision:
Handle: RePEc:tky:fseres:2007cf503
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  1. Richard Blundell & Steve Bond, 1999. "GMM estimation with persistent panel data: an application to production functions," IFS Working Papers W99/04, Institute for Fiscal Studies.
  2. 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.
  3. 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.
  4. repec:cup:cbooks:9780521818551 is not listed on IDEAS
  5. repec:cup:cbooks:9780521522717 is not listed on IDEAS
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