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A Simple Estimator for Short Panels with Common Factors

Listed author(s):
  • Arturas Juodis
  • Sarafidis, V.

There is a substantial theoretical literature on the estimation of short panel data models with common factors nowadays. Nevertheless, such advances appear to have remained largely unnoticed by empirical practitioners. A major reason for this casual observation might be that existing approaches are computationally burdensome and difficult to program. This paper puts forward a simple methodology for estimating panels with multiple factors based on the method of moments approach. The underlying idea involves substituting the unobserved factors with time-specific weighted averages of the variables included in the model. The estimation procedure is easy to implement because unobserved variables are superseded with observed data. Furthermore, since the model is effectively parameterized in a more parsimonious way, the resulting estimator can be asymptotically more efficient than existing ones. Notably, our methodology can easily accommodate observed common factors and unbalanced panels, both of which are important empirical scenarios. We apply our approach to a data set involving a large panel of 4,500 households in New South Wales (Australia), and estimate the price elasticity of urban water demand.

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File URL: http://ase.uva.nl/binaries/content/assets/subsites/amsterdam-school-of-economics/research/uva-econometrics/dp-2015/1503.pdf
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Paper provided by Universiteit van Amsterdam, Dept. of Econometrics in its series UvA-Econometrics Working Papers with number 15-03.

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Date of creation: 30 Oct 2015
Handle: RePEc:ame:wpaper:1503
Contact details of provider: Postal:
Dept. of Econometrics, Universiteit van Amsterdam, Valckenierstraat 65, NL - 1018 XE Amsterdam, The Netherlands

Web page: http://www.ase.uva.nl/uva-econometrics
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  8. Kazuhiko Hayakawa & Vanessa Smith & M. Hashem Pesaran, 2014. "Transformed Maximum Likelihood Estimation of Short Dynamic Panel Data Models with interactive effects," Cambridge Working Papers in Economics 1412, Faculty of Economics, University of Cambridge.
  9. Juodis, Arturas & Sarafidis, Vasilis, 2014. "Fixed T Dynamic Panel Data Estimators with Multi-Factor Errors," MPRA Paper 57659, University Library of Munich, Germany.
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  15. Camba-Méndez, Gonzalo & Kapetanios, George, 2008. "Statistical tests and estimators of the rank of a matrix and their applications in econometric modelling," Working Paper Series 850, European Central Bank.
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  18. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
  19. Robertson, Donald & Sarafidis, Vasilis & Westerlund, Joakim, 2014. "GMM Unit Root Inference in Generally Trending and Cross-Correlated Dynamic Panels," MPRA Paper 53419, University Library of Munich, Germany.
  20. Karabiyik H. & Urbain J.R.Y.J. & Westerlund J., 2014. "CCE estimation of factor-augmented regression models with more factors than observables," Research Memorandum 007, Maastricht University, Graduate School of Business and Economics (GSBE).
  21. Barry Abrams & Santharajah Kumaradevan & Vasilis Sarafidis & Frank Spaninks, 2012. "An Econometric Assessment of Pricing Sydney’s Residential Water Use," The Economic Record, The Economic Society of Australia, vol. 88(280), pages 89-105, 03.
  22. Eduardo Araral & Yahua Wang, 2013. "Water demand management: review of literature and comparison in South-East Asia," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 29(3), pages 434-450, September.
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  24. Bai, Jushan, 2013. "Likelihood approach to dynamic panel models with interactive effects," MPRA Paper 50267, University Library of Munich, Germany.
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