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First Difference Transformation in Panel VAR models: Robustness, Estimation and Inference

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  • Arturas Juodis

Abstract

This paper considers estimation of Panel Vectors Autoregressive Models of order 1 (PVAR(1)) with possible cross-sectional heteroscedasticity in the error terms. We focus on fixed T consistent estimation methods in First differences (FD) with or without additional strictly exogenous regressors. Additional results for the Panel FD OLS estimator and the FDLS estimator of Han and Phillips (2010) are provided. In the covariance stationary case it is shown that the univariate moment conditions of the latter estimator are violated for general parameter matrices in the multivariate case. Furthermore, we simplify the analysis of Binder, Hsiao, and Pesaran (2005) by providing analytical results for the _rst two derivatives of the Transformed Maximum Likelihood (TML) function. We extend the original model by taking into account possible cross-sectional heteroscedasticity and presence of strictly exogenous regressors. Moreover, we show that in the three wave panel the loglikelihood function of the unrestricted TML estimator violates the global identification assumption. The finite-sample performance of the analyzed methods is investigated in a Monte Carlo study. Results indicate that under effect stationarity the TML estimator encounters problems with global identification even for moderate values of T.

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  • Arturas Juodis, 2013. "First Difference Transformation in Panel VAR models: Robustness, Estimation and Inference," UvA-Econometrics Working Papers 13-06, Universiteit van Amsterdam, Dept. of Econometrics.
  • Handle: RePEc:ame:wpaper:1306
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    Cited by:

    1. Kripfganz, Sebastian & Schwarz, Claudia, 2013. "Estimation of Linear Dynamic Panel Data Models with Time-Invariant Regressors," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79756, Verein für Socialpolitik / German Economic Association.
    2. repec:bla:obuest:v:79:y:2017:i:4:p:463-494 is not listed on IDEAS
    3. Hayakawa, Kazuhiko, 2016. "Improved GMM estimation of panel VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 240-264.
    4. Alona Zharova & Wolfgang K. Härdle & Stefan Lessmann, 2017. "Is Scientific Performance a Function of Funds?," SFB 649 Discussion Papers SFB649DP2017-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2015. "Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 188(1), pages 111-134.
    6. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    7. Maurice J.G. Bun & Martin A. Carree & Artūras Juodis, 2017. "On Maximum Likelihood Estimation of Dynamic Panel Data Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 463-494, August.
    8. Arturas Juodis, 2015. "Iterative Bias Correction Procedures Revisited: A Small Scale Monte Carlo Study," UvA-Econometrics Working Papers 15-02, Universiteit van Amsterdam, Dept. of Econometrics.

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