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Exponent of Cross-sectional Dependence: Estimation and Inference

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  • Bailey, N.
  • Kapetanios, G.
  • Pesaran, M. H.

Abstract

An important issue in the analysis of cross-sectional dependence which has received renewed interest in the past few years is the need for a better understanding of the extent and nature of such cross dependencies. In this paper we focus on measures of cross-sectional dependence and how such measures are related to the behaviour of the aggregates defined as cross-sectional averages. We endeavour to determine the rate at which the cross-sectional weighted average of a set of variables appropriately demeaned, tends to zero. One parameterisation sets this to be , for . Given the fashion in which it arises, we refer to as the exponent of cross-sectional dependence. We derive an estimator of from the estimated variance of the cross-sectional average of the variables under consideration. We propose bias corrected estimators, derive their asymptotic properties and consider a number of extensions. We include a detailed Monte Carlo study supporting the theoretical results. Finally, we undertake an empirical investigation of using the S&P 500 data-set, and a large number of macroeconomic variables across and within countries.

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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 1206.

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Date of creation: 23 Jan 2012
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Handle: RePEc:cam:camdae:1206

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  1. Alessandro Rebucci & Ambrogio Cesa-Bianchi & M. Hashem Pesaran & TengTeng Xu, 2012. "China's Emergence in the World Economy and Business Cycles in Latin America," JOURNAL OF LACEA ECONOMIA, LACEA - LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION.
  2. Dees, S. & di Mauro, F. & Pesaran, M.H. & Smith, L.V., 2005. "Exploring the International Linkages of the Euro Area: a Global VAR Analysis," Cambridge Working Papers in Economics 0518, Faculty of Economics, University of Cambridge.
  3. Xavier Gabaix, 2005. "The Granular Origins of Aggregate Fluctuations," 2005 Meeting Papers 470, Society for Economic Dynamics.
  4. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  5. M Hashem Pesaran & Takashi Yamagata, 2012. "Testing CAPM with a Large Number of Assets," Discussion Papers 12/05, Department of Economics, University of York.
  6. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037, September.
  7. Forni, Mario & Lippi, Marco, 2000. "The Generalized Dynamic Factor Model: Representation Theory," CEPR Discussion Papers 2509, C.E.P.R. Discussion Papers.
  8. Eklund, Jana & Kapetanios, George & Price, Simon, 2010. "Forecasting in the presence of recent structural change," Bank of England working papers 406, Bank of England.
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Cited by:
  1. Pesaran, M. Hashem & Chudik, Alexander, 2011. "Aggregation in Large Dynamic Panels," IZA Discussion Papers 5478, Institute for the Study of Labor (IZA).
  2. M. Hashem Pesaran & L. Vanessa Smith & Takashi Yamagata, 2008. "Panel Unit Root Tests in the Presence of a Multifactor Error Structure," Discussion Papers 08/03, Department of Economics, University of York.

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