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Estimation of linear dynamic panel data models with time-invariant regressors

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  • Kripfganz, Sebastian
  • Schwarz, Claudia

Abstract

This paper considers estimation methods and inference for linear dynamic panel data models with unit-specific heterogeneity and a short time dimension. In particular, we focus on the identification of the coefficients of time-invariant variables in a dynamic version of the Hausman and Taylor (1981) model. We propose a two-stage estimation procedure to identify the effectsof time-invariant regressors. We first estimate the coefficients of the time-varying regressors and subsequently regress the first-stage residuals on the time-invariant regressors to recover the coefficients of the latter. Standard errors are adjusted to take into account the first-stage estimation uncertainty. As potential first-stage estimators we discuss generalized method of moments estimators and the transformed likelihood approach of Hsiao, Pesaran, and Tahmiscioglu (2002). Monte Carlo experiments are used to compare the performance of the two-stage approach to various system GMM estimators that obtain all parameter estimates simultaneously. The results are in favor of the two-stage approach. We provide further simulation evidence that GMM estimators with a large number of instruments can be severely biased in finite samples. Reducing the instrument count by collapsing the instrument matrices strongly improves the results while restricting the lag depth does not. Finally, we estimate a dynamic Mincer equation with data from the Panel Study of Income Dynamics to illustrate the approach. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Papers with number 25/2013.

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Date of creation: 2013
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Handle: RePEc:zbw:bubdps:252013

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Keywords: System GMM; Instrument proliferation; Maximum likelihood; Two-stage estimation; Monte Carlo simulation; Dynamic Mincer equation;

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  1. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," IZA Discussion Papers 6583, Institute for the Study of Labor (IZA).
  2. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 13(1), pages 95-126, 02.
  3. Blundell, R. & Bond, S., 1995. "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models," Economics Papers 104, Economics Group, Nuffield College, University of Oxford.
  4. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, Econometric Society, vol. 50(4), pages 1029-54, July.
  5. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 18(1), pages 5-46, January.
  6. Breusch, Trevor & Ward, Michael B & Nguyen, Hoa & Kompas, Tom, 2010. "On the fixed-effects vector decomposition," MPRA Paper 21452, University Library of Munich, Germany.
  7. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780199245291, October.
  8. Corrado Andini, 2010. "A dynamic Mincer equation with an application to Portuguese data," Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 42(16), pages 2091-2098.
  9. Amemiya, Takeshi & MaCurdy, Thomas E, 1986. "Instrumental-Variable Estimation of an Error-Components Model," Econometrica, Econometric Society, Econometric Society, vol. 54(4), pages 869-80, July.
  10. 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, Wiley Blackwell, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  11. David Roodman, 2007. "A Note on the Theme of Too Many Instruments," Working Papers, Center for Global Development 125, Center for Global Development.
  12. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, Elsevier, vol. 68(1), pages 29-51, July.
  13. Corrado Andini, 2007. "Returns to education and wage equations: a dynamic approach," Applied Economics Letters, Taylor & Francis Journals, Taylor & Francis Journals, vol. 14(8), pages 577-579.
  14. Andini, Corrado, 2009. "How Fast Do Wages Adjust to Human-Capital Productivity? Dynamic Panel-Data Evidence from Belgium, Denmark and Finland," IZA Discussion Papers 4583, Institute for the Study of Labor (IZA).
  15. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1.
  16. Spence, A Michael, 1973. "Job Market Signaling," The Quarterly Journal of Economics, MIT Press, MIT Press, vol. 87(3), pages 355-74, August.
  17. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4 National Bureau of Economic Research, Inc.
  18. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, Econometric Society, vol. 46(1), pages 69-85, January.
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Cited by:
  1. Andini, Corrado, 2014. "Persistence Bias and Schooling Returns," IZA Discussion Papers 8143, Institute for the Study of Labor (IZA).
  2. Frieder Kropfhäußer & Marco Sunder, 2014. "A Weighty Issue Revisited: The Dynamic Effect of Body Weight on Earnings and Satisfaction in Germany," SOEPpapers on Multidisciplinary Panel Data Research 635, DIW Berlin, The German Socio-Economic Panel (SOEP).
  3. Kornher, Lukas & Kalkuhl, Matthias, 2013. "Food price volatility in developing countries and its determinants," 53rd Annual Conference, Berlin, Germany, September 25-27, 2013 156132, German Association of Agricultural Economists (GEWISOLA).

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