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Backward Mean Transformation in Panel Data with Predetermined Regressors

Artūras Juodis (Amsterdam School of Economics, University of Amsterdam, Roetersstraat 11, 1018 WB, Amsterdam, The Netherlands)

Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology

ISBN: 978-1-80262-066-5, eISBN: 978-1-80262-065-8

Publication date: 18 January 2022

Abstract

This chapter analyzes the properties of an alternative least-squares based estimator for linear panel data models with general predetermined regressors. This approach uses backward means of regressors to approximate individual specific fixed effects (FE). The author analyzes sufficient conditions for this estimator to be asymptotically efficient, and argue that, in comparison with the FE estimator, the use of backward means leads to a non-trivial bias-variance tradeoff. The author complements theoretical analysis with an extensive Monte Carlo study, where the author finds that some of the currently available results for restricted AR(1) model cannot be easily generalized, and should be extrapolated with caution.

Keywords

Acknowledgements

Acknowledgments

I would like to thank Maurice Bun, Martin Carree, Alex Chudik, Milda Norkutė, Robinson Kruse, Vasilis Sarafidis, Tom Wansbeek, Joakim Westerlund and the participants of the 9th Nordic Econometric Meeting in Tartu, Lund University, and IWEEE Panel Data Workshop in Milano for constructive comments and suggestions. Financial support from the Netherlands Organization for Scientific Research (NWO) under research grant number 45–17–002 is gratefully acknowledged. This chapter previously circulated under title “Bias Reduction with Backward Filtering in Dynamic Panel Data.”

Citation

Juodis, A. (2022), "Backward Mean Transformation in Panel Data with Predetermined Regressors", Chudik, A., Hsiao, C. and Timmermann, A. (Ed.) Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology (Advances in Econometrics, Vol. 43B), Emerald Publishing Limited, Leeds, pp. 103-143. https://doi.org/10.1108/S0731-90532021000043B006

Publisher

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Emerald Publishing Limited

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