Correlation Testing in Time Series, Spatial and Cross-Sectional Data

35 Pages Posted: 13 May 2009

See all articles by Peter M. Robinson

Peter M. Robinson

London School of Economics & Political Science (LSE) - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: January 2009

Abstract

We provide a general class of tests for correlation in time series, spatial, spatiotemporal and cross-sectional data. We motivate our focus by reviewing how computational and theoretical difficulties of point estimation mount as one moves from regularly-spaced time series data, through forms of irregular spacing, and to spatial data of various kinds. A broad class of computationally simple tests is justified. These specialize to Lagrange multiplier tests against parametric departures of various kinds. Their forms are illustrated in case of several models for describing correlation in various kinds of data. The initial focus assumes homoscedasticity, but we also robustify the tests to nonparametric heteroscedasticity.

JEL Classification: C21, C22, C29

Suggested Citation

Robinson, Peter M., Correlation Testing in Time Series, Spatial and Cross-Sectional Data (January 2009). LSE STICERD Research Paper No. EM530. Available at SSRN: https://ssrn.com/abstract=1401779

Peter M. Robinson (Contact Author)

London School of Economics & Political Science (LSE) - Department of Economics ( email )

Houghton Street
London WC2A 2AE
United Kingdom

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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