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Empirical Likelihood Estimation in Dynamic Panel Models

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    Abstract

    This paper proposes and analyses an hybrid of Owen.s (1988, 1990, 1991) Empirical Likelihood (EL) and bootstrap, EL-bootstrap, as an alternative to the General Method of Moments (GMM) within dynamic panel data models. We concentrate on the .nite-sample size properties of their over-identification tests. Our results show that EL-bootstrap may be a good alternative to GMM estimation within this setting. The practical usefulness of our findings is illustrated via application on an AR(1) univariate panel data model with individual e¤ects using the cash-flow series of 174 firms in the United States.

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    File URL: http://www.econ.ed.ac.uk/papers/panel_data_improved_paper.pdf
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    Bibliographic Info

    Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 168.

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    Length: 40
    Date of creation: 22 Aug 2007
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    Handle: RePEc:edn:esedps:168

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    1. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
    2. John Y. Campbell & Motohiro Yogo, 2002. "Efficient Tests of Stock Return Predictability," Harvard Institute of Economic Research Working Papers 1972, Harvard - Institute of Economic Research.
    3. Bronwyn H. Hall & Jacques Mairesse & Benoit Mulkay, 1998. "Does cash flow cause investment and R&D: an exploration using panel data for French, Japanes and United States scientific firms," IFS Working Papers W98/11, Institute for Fiscal Studies.
    4. 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.
    5. Marcelo J. Moreira & Jack R. Porter & Gustavo A. Suarez, 2004. "Bootstrap and Higher-Order Expansion Validity When Instruments May Be Weak," NBER Technical Working Papers 0302, National Bureau of Economic Research, Inc.
    6. Dahlberg, Matz & Johansson, Eva & Tovmo, Per, 2002. "Power Properties of the Sargan Test in the Presence of Measurement Errors in Dynamic Panels," Working Paper Series 2002:13, Uppsala University, Department of Economics.
    7. Maurice Bun & Frank Windmeijer, 2007. "The weak instrument problem of the system GMM estimator in dynamic panel data models," CeMMAP working papers CWP08/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. 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.
    9. Hjalmarsson, Erik, 2005. "Predictive regressions with panel data," Working Papers in Economics 160, University of Gothenburg, Department of Economics.
    10. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-95, November.
    11. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
    12. Kazuhiko Hayakawa, 2005. "Small Sample Bias Propreties of the System GMM Estimator in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d05-82, Institute of Economic Research, Hitotsubashi University.
    13. Maurice J.G. Bun & Jan F. Kiviet, 2002. "The Effects of Dynamic Feedbacks on LS and MM Estimator Accuracy in Panel Data Models," Tinbergen Institute Discussion Papers 02-101/4, Tinbergen Institute, revised 19 Feb 2004.
    14. Frank Windmeijer, 1998. "Efficiency comparisons for a system GMM estimator in dynamic panel data models," IFS Working Papers W98/01, Institute for Fiscal Studies.
    15. Frank Kleibergen, 2004. "Testing Subsets of Structural Parameters in the Instrumental Variables," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 418-423, February.
    16. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, EconWPA, revised 05 Mar 1996.
    17. Steve Bond & Clive Bowsher & Frank Windmeijer, 2001. "Criterion-based inference for GMM in autoregressive panel-data models," IFS Working Papers W01/02, Institute for Fiscal Studies.
    18. Frank Kleibergen, 2002. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Econometrica, Econometric Society, vol. 70(5), pages 1781-1803, September.
    19. Han, Chirok & Phillips, Peter C. B., 2010. "Gmm Estimation For Dynamic Panels With Fixed Effects And Strong Instruments At Unity," Econometric Theory, Cambridge University Press, vol. 26(01), pages 119-151, February.
    20. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    21. Stephen Bond & Céline Nauges & Frank Windmeijer, 2002. "Unit Roots and Identification in Autoregressive Panel Data Models: A Comparison of Alternative Tests," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C5-4, International Conferences on Panel Data.
    22. 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.
    23. Magdalinos, Michael A. & Symeonides, Spyridon D., 1996. "A reinterpretation of the tests of overidentifying restrictions," Journal of Econometrics, Elsevier, vol. 73(2), pages 325-353, August.
    24. Steve Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. 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.
    26. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, 07.
    27. Bowsher, Clive G., 2002. "On testing overidentifying restrictions in dynamic panel data models," Economics Letters, Elsevier, vol. 77(2), pages 211-220, October.
    28. Stephen Bond & Frank Windmeijer, 2002. "Finite Sample Inference for GMM Estimators in Linear Panel Data Models," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C6-3, International Conferences on Panel Data.
    29. 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.
    30. 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.
    31. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    32. Horowitz, Joel L. & Savin, N. E., 2000. "Empirically relevant critical values for hypothesis tests: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 95(2), pages 375-389, April.
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    Cited by:
    1. Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2010. "Empirical Likelihood Block Bootstrapping," Discussion Papers 2010-01, Graduate School of Economics, Hitotsubashi University.

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