Selected Challenges from Spatial Statistics for Spatial Econometricians

Authors

  • Daniel A. Griffith University of Texas at Dallas

DOI:

https://doi.org/10.2478/v10103-012-0027-5

Abstract

Griffith and Paelinck (2011) present selected non-standard spatial statistics and spatial econometrics topics that address issues associated with spatial econometric methodology. This paper addresses the following challenges posed by spatial autocorrelation alluded to and/or derived from the spatial statistics topics of this book: the Gaussian random variable Jacobian term for massive datasets; topological features of georeferenced data; eigenvector spatial filtering-based georeferenced data generating mechanisms; and, interpreting random effects.

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References

Anselin L. (1988), Spatial Econometrics, Kluwer, Dordrecht
Google Scholar

Bentkus V., Bloznelis M., Götze F. (1996), A Berry-Esséen bound for Student’s statistic in the non-i.i.d. case, ‘J. of Theoretical Probability’, Springer, New York, 9
Google Scholar

Besag J. (1974), Spatial interaction and the statistical analysis of lattice systems, ‘J. of the Royal Statistical Society B’, Wiley, New York, 36
Google Scholar

Chaidee N., Tuntapthai M. (2009), Berry-Esséen bounds for random sums of non-i.i.d. random variables, ‘International Mathematical Forum’, m-Hikari, Ruse, 4
Google Scholar

Cliff A., Ord J. (1969), The Problem of Spatial Autocorrelation, [in:] A. Scott (ed.) London Papers in Regional Science, Pion, London
Google Scholar

Curry L. (1967), Quantitative geography, 1967, ‘The Canadian Geographer’, Wiley, New York, 11
Google Scholar

Cvetković D., Rowlinson P. (1990), The largest eigenvalue of a graph: a survey, ‘Linear and Multilinear Algebra’, Taylor & Francis, Abingdon, 28
Google Scholar

Gould P. (1967), On the geographical interpretation of eigenvalues, ‘Transactions, Institute of British Geographers’, Wiley, New York, 42
Google Scholar

Griffith D. (1992), Simplifying the normalizing factor in spatial autoregressions for irregular lattices, ‘Papers in Regional Science’, Wiley, New York, 71
Google Scholar

Griffith D. (2004a), Extreme eigenfunctions of adjacency matrices for planar graphs employed in spatial analyses, ‘Linear Algebra & Its Applications’, Elsevier, Amsterdam, 388
Google Scholar

Griffith G. (2004b), Faster maximum likelihood estimation of very large spatial autoregressive models: an extension of the Smirnov-Anselin result, ‘J. of Statistical Computation and Simulation’, Taylor & Francis, Abingdon, 74
Google Scholar

Griffith D. (2011a), Positive spatial autocorrelation, mixture distributions, and geospatial data histograms, [in:] Y. Leung, B. Lees, C. Chen, C. Zhou, and D. Guo (eds.), ‘Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM 2011)’ , IEEE, Beijing
Google Scholar

Griffith D. (2011b), Positive spatial autocorrelation impacts on attribute variable frequency distributions, ‘Chilean J. of Statistics’, Sociedad Chilena de Estadística, Valparaiso, 2 (2)
Google Scholar

Griffith D., Paelinck J. (2011), Non-standard Spatial Statistics and Spatial Econometrics, Springer-Verlag, Berlin
Google Scholar

Maćkiewic, A., Ratajczak W. (1996), Towards a new definition of topological accessibility, ‘Transportation Research B’, Elsevier, Amsterdam, 30
Google Scholar

Mass C. (1985), Computing and interpreting the adjacency spectrum of traffic networks, ‘Journal of Computational and Applied Mathematics’, Elsevier, Amsterdam, 12&13
Google Scholar

Ord J. (1975), Estimation methods for models of spatial interactions, ‘Journal of the American Statistical Association’, Taylor & Francis, Abingdon, 70
Google Scholar

Pace R., LeSage J. (2004), Chebyshev approximation of log-determinants of spatial weight matrices, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam, 45
Google Scholar

Paelinck J. (2012), Some challenges for spatial econometricians, paper presented at the 2nd International Scientific Conference about Spatial Econometrics and Regional Economic Analys is, University of Lodz, Poland
Google Scholar

Paelinck J., and Klaassen L. (1979), Spatial Econometrics, Saxon House, Farnborough
Google Scholar

Smirnov O., Anselin L. (2001), Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam,, 35
Google Scholar

Smirnov O., Anselin L. (2009), An O(N) parallel method of computing the log-Jacobian of the variable transformation for models with spatial interaction on a lattice, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam, 53
Google Scholar

Walde J., Larch M., Tappeiner G. (2008), Performance contest between MLE and GMM for huge spatial autoregressive models, ‘J. of Statistical Computation and Simulation’, Taylor & Francis, Abingdon, 78
Google Scholar

Zhang Y., Leithead W. (2007), Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression, ‘J. of Statistical Computation and Simulation’, Taylor & Francis, Abingdon, 77
Google Scholar

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Published

2013-03-08

How to Cite

Griffith, D. A. (2013). Selected Challenges from Spatial Statistics for Spatial Econometricians. Comparative Economic Research. Central and Eastern Europe, 15(4), 71–85. https://doi.org/10.2478/v10103-012-0027-5

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