A Principal Components Approach to Cross-Section Dependence in Panels
AbstractThe use of GLS to deal with cross-section dependence in panels is not feasible where N is large relative to T since the disturbance covariance matrix is rank deficient. Neither is it the appropriate response if the dependence results from omitted global variables or common shocks correlated with the included regressors. These can be proxied by the principal components of the residuals from a baseline regression. It is shown that the OLS estimates from a regression augmented by these principal components are unbiased and consistent using sequential limits for large T, large N. Simulations show that this leads to a substantial reduction in bias even for relatively small T and N panels. An empirical application indicates that the impact of cross section dependence seems to strengthen the case for long run PPP.
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Bibliographic InfoPaper provided by International Conferences on Panel Data in its series 10th International Conference on Panel Data, Berlin, July 5-6, 2002 with number B5-3.
Date of creation: Mar 2002
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Factor analysis; global shocks; omittted variable bias;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- F31 - International Economics - - International Finance - - - Foreign Exchange
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-07-04 (All new papers)
- NEP-ECM-2002-07-10 (Econometrics)
- NEP-ETS-2002-07-04 (Econometric Time Series)
- NEP-IFN-2002-07-04 (International Finance)
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