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Weak and strong cross‐section dependence and estimation of large panels

  • Alexander Chudik
  • M. Hashem Pesaran
  • Elisa Tosetti

This paper introduces the concepts of time‐specific weak and strong cross‐section dependence, and investigates how these notions are related to the concepts of weak, strong and semi‐strong common factors, frequently used for modelling residual cross‐section correlations in panel data models. It then focuses on the problems of estimating slope coefficients in large panels, where cross‐section units are subject to possibly a large number of unobserved common factors. It is established that the common correlated effects (CCE) estimator introduced by Pesaran remains asymptotically normal under certain conditions on factor loadings of an infinite factor error structure, including cases where methods relying on principal components fail. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.

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File URL: http://hdl.handle.net/10.1111/j.1368-423X.2010.00330.x
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Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 14 (2011)
Issue (Month): 1 (February)
Pages: C45-C90

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Handle: RePEc:ect:emjrnl:v:14:y:2011:i:1:p:c45-c90
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  1. Coakley, Jerry & Fuertes, Ana-Maria & Smith, Ron, 2006. "Unobserved heterogeneity in panel time series models," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2361-2380, May.
  2. Kapetanios, George & Pesaran, M. Hashem & Yamagata, Takashi, 2006. "Panels with Nonstationary Multifactor Error Structures," IZA Discussion Papers 2243, Institute for the Study of Labor (IZA).
  3. Jerry Coakley & Ana-Maria Fuertes & Ron Smith, 2002. "A Principal Components Approach to Cross-Section Dependence in Panels," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B5-3, International Conferences on Panel Data.
  4. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  5. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
  6. Chamberlain, Gary, 1983. "Funds, Factors, and Diversification in Arbitrage Pricing Models," Econometrica, Econometric Society, vol. 51(5), pages 1305-23, September.
  7. Chudik, Alexander & Pesaran, Hashem, 2009. "Infinite-dimensional VARs and factor models," Working Paper Series 0998, European Central Bank.
  8. George Kapetanios & Massimiliano Marcellino, 2006. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," Working Papers 577, Queen Mary University of London, School of Economics and Finance.
  9. Forni, Mario & Lippi, Marco, 2000. "The Generalized Dynamic Factor Model: Representation Theory," CEPR Discussion Papers 2509, C.E.P.R. Discussion Papers.
  10. Ross, Stephen A., 1976. "The arbitrage theory of capital asset pricing," Journal of Economic Theory, Elsevier, vol. 13(3), pages 341-360, December.
  11. Ingersoll, Jonathan E, Jr, 1984. " Some Results in the Theory of Arbitrage Pricing," Journal of Finance, American Finance Association, vol. 39(4), pages 1021-39, September.
  12. Gregory, Allan W. & Head, Allen C., 1999. "Common and country-specific fluctuations in productivity, investment, and the current account," Journal of Monetary Economics, Elsevier, vol. 44(3), pages 423-451, December.
  13. Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 453-73, July.
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