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Spatial correlations in panel data

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Author Info

  • Driscoll, John
  • Kraay, Aart

Abstract

In many empirical applications involving combined time-series and cross-sectional data, the residuals from different cross-sectional units are likely to be correlated with one another. This is the case in applications in macroeconomics and international economics where the cross-sectional units may be countries, states, or regions observed over time. Spatial correlations among such cross-sections may arise for a number of reasons, ranging from observed common shocks such as terms of trade oil shocks, to unobserved contagion or neighborhood effects which propagate across countries in complex ways. The authors observe that presence of such spatial correlations in residuals complicates standard inference procedures that combine time-series and cross-sectional data since these techniques typically require the assumption that the cross-sectional units are independent. When this assumption is violated, estimates of standard errors are inconsistent, and hence are not useful for inference. And standard correction for spatial correlations will be valid only if spatial correlations are of particular restrictive forms. The authors propose a correlation for spatial correlations that does not require strong assumptions concerning their form and how show it is superior to a number of commonly used alternatives.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 1553.

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Date of creation: 31 Dec 1995
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Handle: RePEc:wbk:wbrwps:1553

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Keywords: Sanitation and Sewerage; Statistical&Mathematical Sciences; Scientific Research&Science Parks; Information Technology; Environmental Economics&Policies; Statistical&Mathematical Sciences; Scientific Research&Science Parks; Science Education; Econometrics; Information Technology;

References

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  1. Froot, Kenneth A., 1989. "Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(03), pages 333-355, September.
  2. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
  3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  4. Bruce N. Lehmann, 1986. "Residual Risk Revisited," NBER Working Papers 1908, National Bureau of Economic Research, Inc.
  5. Keane, Michael P & Runkle, David E, 1990. "Testing the Rationality of Price Forecasts: New Evidence from Panel Data," American Economic Review, American Economic Association, vol. 80(4), pages 714-35, September.
  6. Lehmann, Bruce N., 1990. "Residual risk revisited," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 71-97.
  7. Kraay, Aart & Ventura, Jaume, 1995. "Trade and fluctuations," Policy Research Working Paper Series 1560, The World Bank.
  8. Al-Najjar, Nabil Ibraheem, 1995. "Decomposition and Characterization of Risk with a Continuum of Random Variables," Econometrica, Econometric Society, vol. 63(5), pages 1195-1224, September.
  9. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  10. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
  11. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-36, May-June.
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Cited by:
  1. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.

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