Mur Lacambra, Jesús and Herrera Gómez, Marcos and Ruiz Marin, Manuel (2013): Selecting the W Matrix: Parametric vs. Non Parametric Approaches.

PDF
MPRA_paper_71181.pdf Download (417kB)  Preview 
Abstract
In spatial econometrics, it is customary to specify a weighting matrix, the socalled W matrix, by choosing one matrix from a finite set of matrices. The decision is extremely important because, if the W matrix is misspecified, the estimates are likely to be biased and inconsistent. However, the procedure to select W is not well defined and, usually, it reflects the judgments of the user. In this paper, we revise the literature looking for criteria to help with this problem. Also, a new nonparametric procedure is introduced. Our proposal is based on a measure of the information, conditional entropy. We compare these alternatives by means of a Monte Carlo experiment.
Item Type:  MPRA Paper 

Original Title:  Selecting the W Matrix: Parametric vs. Non Parametric Approaches 
Language:  English 
Keywords:  Spatial weighting matrix; selection models; parametric methods; non parametric methods 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C31  CrossSectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction Models C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C50  General 
Item ID:  71181 
Depositing User:  marcos herrera 
Date Deposited:  11 May 2016 15:26 
Last Modified:  04 Oct 2019 18:46 
References:  Akaike, H. (1973): Information Theory and an Extension of the Maximum Likelihood Principle. In Petrow, B. and F. Csaki (eds): 2nd International Symposium on Information Theory (pp 267281). Budapest: Akademiai Kiodo. Aldstadt, J. and A. Getis (2006): Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters. Geographical Analysis 38 327343. Ancot, L, J. Paelinck, L. Klaassen and W Molle (1982): Topics in Regional Development Modelling. In M. Albegov, Å. Andersson and F. Snickars (eds, pp.341359), Regional Development Modelling in Theory and Practice. Amsterdam: North Holland. Anselin, L. (1984): Specification Tests on the Structure of Interaction in Spatial Econometric Models. Papers, Regional Science Association 54 165182. Anselin L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer. Anselin, L. (2002): Under the Hood: Issues in the Specification and Interpretation of Spatial Regression Models. Agricultural Economics 17 247–267. Bavaud, F. (1998): Models for Spatial Weights: a Systematic Look. Geographical Analysis 30 153171. Beenstock M., Ben Zeev N. and Felsenstein D (2010): Nonparametric Estimation of the Spatial Connectivity Matrix using Spatial Panel Data. Working Paper, Department of Geography, Hebrew University of Jerusalem. Bhattacharjee A, JensenButler C (2006): Estimation of spatial weights matrix, with an application to diffusion in housing demand. Working Paper, School of Economics and Finance, University of St.Andrews, UK. Bodson, P. and D. Peters (1975): Estimation of the Coefficients of a Linear Regression in the Presence of Spatial Autocorrelation: An Application to a Belgium Labor Demand Function. Environment and Planning A 7 455472. Burridge, P. (2011): Improving the J test in the SARAR model by likelihoodbased estimation. Working Paper; Department of Economics and Related Studies, University of York . Burridge, P. and Fingleton, B. (2010): Bootstrap inference in spatial econometrics: the Jtest. Spatial Economic Analysis 5 93119. Conley, T. and F. Molinari (2007): Spatial Correlation Robust Inference with Errors in Location or Distance. Journal of Econometrics, 140 7696. Corrado, L. and B. Fingleton (2011): Where is Economics in Spatial Econometrics? Working Paper; Department of Economics, University of Strathclyde. Dacey M. (1965): A Review on Measures of Contiguity for Two and kColor Maps. In J. Berry and D. Marble (eds.): A Reader in Statistical Geography. Englewood Cliffs: PrenticeHall. Fernández E., Mayor M. and J. Rodríguez (2009): Estimating spatial autoregressive models by GMEGCE techniques. International Regional Science Review, 32 148172. Folmer, H. and J. Oud (2008): How to get rid of W? A latent variable approach to modeling spatially lagged variables. Environment and Planning A 40 25262538 Getis A, and J. Aldstadt (2004): Constructing the Spatial Weights Matrix Using a Local Statistic Spatial. Geographical Analysis, 36 90104. Haining, R. (2003): Spatial Data Analysis. Cambridge: Cambridge University Press. Hansen, B. (2007): Least Squares Model Averaging. Econometrica, 75, 11751189. Hansen, B. and J. Racine (2010): Jackknife Model Averaging. Working Paper, Department of Economics, McMaster University Hepple, L. (1995a): Bayesian Techniques in Spatial and Network Econometrics: 1 Model Comparison and Posterior Odds. Environment and Planning A, 27, 447–469. Hepple, L. (1995b): Bayesian Techniques in Spatial and Network Econometrics: 2 Computational Methods and Algorithms. Environment and Planning A, 27, 615–644. Kelejian, H (2008): A spatial Jtest for Model Specification Against a Single or a Set of NonNested Alternatives. Letters in Spatial and Resource Sciences, 1 311. Kooijman, S. (1976): Some Remarks on the Statistical Analysis of Grids Especially with Respect to Ecology. Annals of Systems Research 5. Leamer, E (1978): Specification Searches: Ad Hoc Inference with Non Experimental Data. New York: John Wiley and Sons, Inc. Leenders, R (2002): Modeling Social Influence through Network Autocorrelation: Constructing the Weight Matrix. Social Networks, 24, 2147. Lesage, J. and K. Pace (2009): Introduction to Spatial Econometrics. Boca Raton: CRC Press. Lesage, J. and O. Parent (2007): Bayesian Model Averaging for Spatial Econometric Models. Geographical Analysis, 39, 241267. Matilla, M. and M. Ruiz (2008): A nonparametric independence test using permutation entropy. Journal of Econometrics, 144, 139155. Moran, P. (1948): The Interpretation of Statistical Maps. Journal of the Royal Statistical Society B 10 243251. Mur, J. and J Paelinck (2010): Deriving the Wmatrix via pmedian complete correlation analysis of residuals.The Annals of Regional Science, DOI: 10.1007/s0016801003793. Openshaw, S. (1977): Optimal Zoning Systems for Spatial Interaction Models. Environment and Planning A 9, 16984. Ord K. (1975): Estimation Methods for Models of Spatial Interaction. Journal of the American Statistical Association. 70 120126. Paci, R. and S. Usai (2009): Knowledge flows across European regions. The Annals of Regional Science, 43 669690. Paelinck, J and L. Klaassen (1979): Spatial Econometrics. Farnborough: Saxon House Piras, G and N Lozano (2010): Spatial Jtest: some Monte Carlo evidence. Statistics and Computing, DOI: 10.1007/s112220109215y. Tobler W. (1970): A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 234240. Whittle, P. (1954): On Stationary Processes in the Plane. Biometrika, 41 434449. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/71181 