Exponent of Cross-Sectional Dependence: Estimation and Inference

Natalia Bailey, George Kapetanios, M Hashem Pesaran

Research output: Contribution to journalArticlepeer-review

124 Citations (Scopus)

Abstract

This paper provides a characterisation of the degree of cross-sectional dependence in a two dimensional array, {xit,i = 1,2,...N;t = 1,2,...,T} in terms of the rate at which the variance of the cross-sectional average of the observed data varies with N. Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of xt=(x1t,x2t,...,xNt)′ rises with N. We represent the degree of cross-sectional dependence by α, which we refer to as the ‘exponent of cross-sectional dependence’, and define it by the standard deviation, math formula, where math formula is a simple cross-sectional average of xit. We propose bias corrected estimators, derive their asymptotic properties for α > 1/2 and consider a number of extensions. We include a detailed Monte Carlo simulation study supporting the theoretical results. We also provide a number of empirical applications investigating the degree of inter-linkages of real and financial variables in the global economy.
Original languageEnglish
JournalJOURNAL OF APPLIED ECONOMETRICS
Volume31
Issue number6
DOIs
Publication statusE-pub ahead of print - 2 Oct 2016

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