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THE CORRELATION STRUCTURE OF SPATIAL AUTOREGRESSIONS

Published online by Cambridge University Press:  27 April 2012

Federico Martellosio*
Affiliation:
University of Reading
*
*Address correspondence to Federico Martellosio, Department of Economics, University of Reading, Whiteknights, Reading RG6 6AA, UK; e-mail: f.martellosio@reading.ac.uk.

Abstract

This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate properties of W to correlation properties of SAR(1) models defined on irregular lattices.

Type
Miscellanea
Copyright
Copyright © Cambridge University Press 2012 

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References

REFERENCES

Abramowitz, A. & Stegun, S. (1979) Handbook of Mathematical Functions. Nauka.Google Scholar
Anselin, L. (1988) Spatial Econometrics: Methods and Models. Kluwer.CrossRefGoogle Scholar
Bartlett, M. (1975) The Statistical Analysis of Spatial Pattern. Chapman-Hall.Google Scholar
Bavaud, F. (1998) Models for spatial weights: A systematic look. Geographical Analysis 50, 155–71.Google Scholar
Besag, J.E. (1972) On the correlation structure of some two-dimensional stationary processes. Biometrika 59, 4348.CrossRefGoogle Scholar
Besag, J.E. (1974) Spatial interaction and the statistical analysis of lattice data. Journal of the Royal Statistical Society B 36, 192236.Google Scholar
Biggs, N.L. (1993) Algebraic Graph Theory, 2nd ed. Cambridge University Press.Google Scholar
Bonacich, P. (1972) Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2, 113120.Google Scholar
Case, A. (1992) Neighborhood influence and technological change. Regional Science and Urban Economics 22, 491508.CrossRefGoogle Scholar
Cliff, A.D. & Ord, J.K. (1981) Spatial Processes: Models and Applications. Pion.Google Scholar
Cressie, N. (1993) Statistics for Spatial Data. Wiley.Google Scholar
Cressie, N., Kaiser, M.S., Daniels, M.J., Aldworth, J., Lee, J., Lahiri, S.N., & Cox, L.H. (1999) Spatial analysis of particulate matter in an urban environment. In Gomez-Hernandez, J., Soares, A., & Froidevaux, R. (eds.), geoENV II - Geostatistics for Environmental Applications, pp. 4152. Kluwer.CrossRefGoogle Scholar
Godsil, C. (1993) Algebraic Combinatorics. Chapman & Hall.Google Scholar
Griffith, D.A. & Csillag, F. (1993) Exploring relationships between semivariogram and spatial autoregressive models. Papers in Regional Science 72, 283295.CrossRefGoogle Scholar
Griffith, D.A. & Lagona, F. (1998) On the quality of likelihood based estimators in spatial autoregressive models when the data dependence structure is misspecified. Journal of Statistical Planning and Inference 69, 153174.CrossRefGoogle Scholar
Harary, F.H. (1969) Graph Theory. Addison-Wesley.CrossRefGoogle Scholar
Horn, R. & Johnson, C.R. (1985) Matrix Analysis. Cambridge University Press.CrossRefGoogle Scholar
Kato, T. (1995) Perturbation Theory for Linear Operators. Springer.CrossRefGoogle Scholar
Kelejian, H.H. & Prucha, I.R. (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics 157, 5367.CrossRefGoogle ScholarPubMed
Lee, L.F. & Yu, J. (2011) Near Unit Root in the Spatial Autoregressive Model. Manuscript, The Ohio State University.Google Scholar
LeSage, J. & Pace, R.K. (2009) Introduction to Spatial Econometrics. Taylor and Francis/CRC.Google Scholar
Martellosio, F. (2011) Efficiency of the OLS estimator in the vicinity of a spatial unit root. Statistics & Probability Letters 81, 12851291.CrossRefGoogle Scholar
Song, H.R., Fuentes, M., & Ghosh, S. (2008) A comparative study of Gaussian geostatistical models and Gaussian Markov random field models. Journal of Multivariate Analysis 99, 16811697.CrossRefGoogle Scholar
Wall, M.M. (2004) A close look at the spatial structure implied by the CAR and SAR models. Journal of Statistical Planning and Inference 121, 311324.Google Scholar
Whittle, P. (1954) On stationary processes in the plane. Biometrika 41, 434449.CrossRefGoogle Scholar