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The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models

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Author Info
Maurice J.G. Bun
Frank Windmeijer ()

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Abstract

The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error. However, we show in this paper that in the covariance stationary panel data AR(1) model the expected values of the concentration parameters in the differenced and levels equations for the crosssection at time t are the same when the variances of the individual heterogeneity and idiosyncratic errors are the same. This indicates a weak instrument problem also for the equation in levels. We show that the 2SLS biases relative to that of the OLS biases are then similar for the equations in differences and levels, as are the size distortions of the Wald tests. These results are shown in a Monte Carlo study to extend to the panel data system GMM estimator.

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Paper provided by Department of Economics, University of Bristol, UK in its series Bristol Economics Discussion Papers with number 07/595.

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Length: 29 pages
Date of creation: Mar 2007
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Handle: RePEc:bri:uobdis:07/595

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Related research
Keywords: Dynamic Panel Data; System GMM; Weak Instruments;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June. [Downloadable!] (restricted)
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  2. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Blackwell Publishing, vol. 58(2), pages 277-97, April. [Downloadable!] (restricted)
  3. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-80, July.
  4. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  5. Bond, Stephen Roy & Hoeffler, Anke & Temple, Jonathan, 2001. "GMM Estimation of Empirical Growth Models," CEPR Discussion Papers 3048, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  6. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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  8. Gabriel A. Picone & Frank Sloan & Justin G. Trogdon, 2004. "The effect of the tobacco settlement and smoking bans on alcohol consumption," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1063-1080. [Downloadable!]
  9. Richard Blundell & Steve Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies. [Downloadable!]
  10. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, 07. [Downloadable!] (restricted)
  11. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April. [Downloadable!] (restricted)
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  12. Whitney Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  13. Griffith, Rachel & Harrison, Rupert & Van Reenen, John, 2004. "How Special is the Special Relationship? Using the Impact of US R&D Spillovers on UK Firms as a Test of Technology Sourcing," CEPR Discussion Papers 4698, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  14. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August. [Downloadable!] (restricted)
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  15. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
  16. Levine, Ross & Loayza, Norman & Beck, Thorsten, 2000. "Financial intermediation and growth: Causality and causes," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 31-77, August. [Downloadable!] (restricted)
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  17. Chirok Han & Peter C. B. Phillips, 2006. "GMM with Many Moment Conditions," Econometrica, Econometric Society, vol. 74(1), pages 147-192, 01. [Downloadable!] (restricted)
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  18. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Blackwell Publishing, vol. 70(2), pages 317-341, 04. [Downloadable!] (restricted)
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  19. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May. [Downloadable!] (restricted)
  20. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, 07. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Wiji Arulampalam & Michael P Devereux & Giorgia Maffini, 2007. "The Direct Incidence of Corporate Income Tax on Wages," Working Papers 0707, Oxford University Centre for Business Taxation. [Downloadable!]
  2. Wiji Arulampalam & Michael P Devereux & Giorgia Maffini, 2009. "The Direct Incidence of Corporate Income Tax on Wages," Working Papers 0917, Oxford University Centre for Business Taxation. [Downloadable!]
  3. 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. [Downloadable!]
  4. Angelica Gonzalez, 2007. "Empirical Likelihood Estimation in Dynamic Panel Models," ESE Discussion Papers 168, Edinburgh School of Economics, University of Edinburgh. [Downloadable!]
  5. Brülhart, Marius & Mathys, Nicole Andréa, 2007. "Sectoral Agglomeration Economies in a Panel of European Regions," CEPR Discussion Papers 6410, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  6. Marius BRÜLHART & Federica SBERGAMI, 2008. "Agglomeration and Growth : Cross-Country Evidence," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 08.04, Université de Lausanne, Faculté des HEC, DEEP. [Downloadable!]
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