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Correlation testing in time series, spatial and cross-sectional data

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  • Peter Robinson

    (Institute for Fiscal Studies and London School of Economics)

Abstract

We provide a general class of tests for correlation in time series, spatial, spatio-temporal 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 justiied. These specialize 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.

Suggested Citation

  • Peter Robinson, 2007. "Correlation testing in time series, spatial and cross-sectional data," CeMMAP working papers CWP01/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:01/07
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    File URL: http://cemmap.ifs.org.uk/wps/cwp107.pdf
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    References listed on IDEAS

    as
    1. Robinson, P. M., 1991. "Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression," Journal of Econometrics, Elsevier, vol. 47(1), pages 67-84, January.
    2. Robinson, P.M. & Vidal Sanz, J., 2006. "Modified Whittle estimation of multilateral models on a lattice," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1090-1120, May.
    3. Sargan, J D & Drettakis, E G, 1974. "Missing Data in an Autoregressive Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 39-58, February.
    4. Badi H. Baltagi & Dong Li, 2001. "LM Tests for Functional Form and Spatial Error Correlation," International Regional Science Review, , vol. 24(2), pages 194-225, April.
    5. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    6. Lee, Lung-Fei, 2002. "Consistency And Efficiency Of Least Squares Estimation For Mixed Regressive, Spatial Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 252-277, April.
    7. Robinson, P. M., 1977. "Estimation of a time series model from unequally spaced data," Stochastic Processes and their Applications, Elsevier, vol. 6(1), pages 9-24, November.
    8. Badi Baltagi & Dong Li, 2000. "LM Tests for Functional Form and Spatial Correlation," Econometric Society World Congress 2000 Contributed Papers 0099, Econometric Society.
    9. Harry H. Kelejian & Dennis P. Robinson, 2004. "The Influence of Spatially Correlated Heteroskedasticity on Tests for Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 4, pages 79-97, Springer.
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    Citations

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    Cited by:

    1. Peter Robinson, 2008. "Large-sample inference on spatial dependence," CeMMAP working papers CWP29/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Peter M Robinson, 2009. "Large-Sample Inference on SpatialDependence," STICERD - Econometrics Paper Series 533, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Robinson, Peter, 2008. "Large-sample inference on spatial dependence," LSE Research Online Documents on Economics 25472, London School of Economics and Political Science, LSE Library.

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    More about this item

    Keywords

    Correlation; heteroscedasticity; Lagrange multiplier tests.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

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