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Panels with Nonstationary Multifactor Error Structures Author info | Abstract | Publisher info | Download info | Related research | Statistics George Kapetanios () (Queen Mary, University of London)
M. Hashem Pesaran (Cambridge University and Trinity College, Cambridge)
Takashi Yamagata (Cambridge University)
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The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.
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Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number
569.
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Date of creation: Sep 2006Date of revision:
Handle: RePEc:qmw:qmwecw:wp569Contact details of provider: Postal: London E1 4NS Phone: +44 (0) 20 7882 5096 Fax: +44 (0) 20 8983 3580 Web page: http://www.econ.qmul.ac.uk More information through EDIRC
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Keywords: Cross section dependence Large panels Unit roots Principal components Common correlated effects Other versions of this item:
Paper Kapetanios, G. & Pesaran, M.H. & Yamagata, T., 2006.
"Panels with Nonstationary Multifactor Error Structures ,"
Cambridge Working Papers in Economics
0651, Faculty of Economics, University of Cambridge.
[Downloadable!] George Kapetanios & M. Hashem Pesaran & Takashi Yamagata, 2006.
"Panels with Nonstationary Multifactor Error Structures ,"
IZA Discussion Papers
2243, Institute for the Study of Labor (IZA).
[Downloadable!] George Kapetanios & M. Hashem Pesaran & Takashi Yamagata, 2006.
"Panels with Nonstationary Multifactor Error Structures ,"
CESifo Working Paper Series
CESifo Working Paper No. , CESifo GmbH.
[Downloadable!] Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
This paper has been announced in the following NEP Reports :
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10th International Conference on Panel Data, Berlin, July 5-6, 2002
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Pesaran, M.H. & Weiner, S.M., 2001.
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[Downloadable!] M. Hashem Pesaran & Til Schuermann & Scott M. Weiner, 2001.
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10th International Conference on Panel Data, Berlin, July 5-6, 2002
B4-1, International Conferences on Panel Data.
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"Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model ,"
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"Macroeconomic Forecasting Using Diffusion Indexes ,"
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Other versions: Connor, Gregory & Korajczyk, Robert A., 1986.
"Performance measurement with the arbitrage pricing theory : A new framework for analysis ,"
Journal of Financial Economics ,
Elsevier, vol. 15(3), pages 373-394, March.
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Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models ,"
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Econometric Society, vol. 70(1), pages 191-221, January.
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references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
Sean Holly & M. Hashem Pesaran & Takashi Yamagata, 2006.
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"Large Panels with Common Factors and Spatial Correlations ,"
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James Smith, .
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