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Initial conditions and moment restrictions in dynamic panel data model

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  • R Blundell
  • Steven Bond

Abstract

In this paper we consider estimation of the autoregressive error components model. When the autoregressive parameter is moderately large and the number of time series observations is moderately small, the usual Generalised Methods of Moments (GMM) estimator obtained after first differencing has been found to be poorly behaved. Here we consider alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. We consider two approaches to estimation. The first approach extends the model by adding the observed initial values as an extra regressor. This allows consistent estimates to be obtained by error-components GLS. This estimator is shown to be equivalent to the optimal GMM estimator for the normal homoskedastic error components model. The second approach considers a mild restriction on the initial condition process under which lagged differences in the dependent variable can be used to construct linear moment conditions in the levels equations. The complete set of moment conditions can then be exploited by a linear GMM estimator in a system of first-differenced and levels equations, rendering the non-linear moment conditions redundant for estimation. This estimator is strictly more efficient than non-linear GMM when the additional restriction is valid. Monte Carlo simulations are reported which demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, especially for high values of the autoregressive parameter.
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(This abstract was borrowed from another version of this item.)
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(This abstract was borrowed from another version of this item.)

Suggested Citation

  • R Blundell & Steven Bond, "undated". "Initial conditions and moment restrictions in dynamic panel data model," Economics Papers W14&104., Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:9614
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    References listed on IDEAS

    as
    1. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters,in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27 World Scientific Publishing Co. Pte. Ltd..
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
    4. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    5. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1989. "The Revenues-Expenditures Nexus: Evidence from Local Government Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(2), pages 415-429, May.
    6. Crepon, Bruno & Kramarz, Francis & Trognon, Alain, 1997. "Parameters of interest, nuisance parameters and orthogonality conditions An application to autoregressive error component models," Journal of Econometrics, Elsevier, vol. 82(1), pages 135-156.
    7. Nelson, Charles R & Startz, Richard, 1990. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 58(4), pages 967-976, July.
    8. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    9. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    10. Prucha, Ingmar R, 1984. "On the Asymptotic Efficiency of Feasible Aitken Estimators for Seemingly Unrelated Regression Models with Error Components," Econometrica, Econometric Society, vol. 52(1), pages 203-207, January.
    11. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
    12. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    13. Sevestre, P. & Trognon, A., 1985. "A note on autoregressive error components models," Journal of Econometrics, Elsevier, vol. 28(2), pages 231-245, May.
    14. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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