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Correlation Testing in Time Series, SpatialandCross-Sectional Data

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  • Peter M Robinson

Abstract

We provide a general class of tests for correlation in time series, spatial, spatiotemporaland cross-sectional data. We motivate our focus by reviewing howcomputational and theoretical difficulties of point estimation mount as one movesfrom regularly-spaced time series data, through forms of irregular spacing, and tospatial data of various kinds. A broad class of computationally simple tests isjustified. These specialize to Lagrange multiplier tests against parametric departuresof various kinds. Their forms are illustrated in case of several models for describingcorrelation in various kinds of data. The initial focus assumes homoscedasticity, butwe also robustify the tests to nonparametric heteroscedasticity.

Suggested Citation

  • Peter M Robinson, 2009. "Correlation Testing in Time Series, SpatialandCross-Sectional Data," STICERD - Econometrics Paper Series 530, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:530
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    4. 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.
    5. 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.
    6. Peter Robinson, 2006. "Efficient estimation of the semiparametric spatial autoregressive model," CeMMAP working papers CWP08/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. 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.
    8. 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.
    9. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
    10. 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.
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    More about this item

    Keywords

    heteroscedasticity; Lagrange multiplier tests.;

    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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