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| 1: | LeSage Synopsis Spatial Econometrics James LeSage This text provides an introduction to spatial econometrics as well as a set of MATLAB functions that implement a host of spatial econometric estimation methods. The intended audience is faculty and students involved in modeling spatial data sets using spatial econometric methods. The MATLAB functions described in this book have been used in my own research as well as teaching both undergraduate and graduate econometrics courses. Toolboxes are the name given by the MathWorks to related sets of MATLAB functions aimed at solving a particular class of problems. Toolboxes of functions useful in signal processing, optimization, statistics, finance and a host of other areas are available from the MathWorks as add-ons to the standard MATLAB software distribution. I use the term Econometrics Toolbox to refer to my collection of function libraries described in a manual entitled Applied Econometrics using MATLAB available at http:/www.econ.utoledo.edu. ... http://www.rri.wvu.edu/WebBook/Synopsis/LeSage.htm |
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| 2: | spatial econometrics library [Return to Master Index] - spatial econometrics functions - casetti - Casetti's spatial expansion model darp - Casetti's darp model far - 1st order spatial AR model - y = pWy + e far_g - Gibbs sampling Bayesian FAR model gwr - geographically weighted regression bgwr - Bayesian GWR lmerror - LM error statistic regression lmerror - LM error statistic sar model lmerror - likelihood ratio statistic moran - Moran's I-statistic sac - general spatial model - y = p*W1*y + u, u = c*W2*u + e sac_g - Gibbs sampling Bayesian SAC model sart_g - Gibbs sampling Bayesian SAC Tobit model sarp_g - Gibbs sampling Bayesian SAC Probit model sar - spatial AR model - y = p*W*y + X*b + e sar_g - Gibbs sampling Bayesian SAR model sart_g - Gibbs sampling Bayesian SAR Tobit model sarp_g - Gibbs sampling Bayesian SAR Probit model sem - spatial error model - y = X*b - p*W*X*b + e sem_g - Gibbs sampling Bayesian SEM model semo - spatial error model optimization rather than iteration sart_g - Gibbs ... http://www.rri.wvu.edu/... age/etoolbox/spatial/contents.html |
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| 3: | Position Paper Luc Anselin Bruton School of Development Studies University of Texas at Dallas Position Statement Curriculum Vitae Address Position Statement GIS, Spatial Econometrics and Social Science Research The subset of the domain of spatial analysis that pertains to the statistical analysis of spatially referenced data has recently gained a growing acceptance as a methodology in the mainstream social sciences. I will focus my remarks on this specific issue, leaving the discussion of aspects of spatial analysis such as optimization and decision support systems to others. The recent dissemination of a spatial analytic perspective in the social sciences (outside of the discipline of geography) is often attributed to the rapid spread of GIS technology to the desktop and the availability of a vast array of geographically referenced socio-economic data. This has led to the use of GIS for data organization and visualization as well as increasingly in an inductive approach to exploring ... http://www.ncgia.ucsb.edu/... sa_workshop/papers/anselin.html |
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| 4: | Index for Spatial Data Sets Spatial econometrics data sets Anselin' Columbus neighborhood crime 49 observations 3 variables plus x-y coordinates. 1st order contiguity matrix for Columbus data a 49 x 49 matrix (row standardized). Harrison-Rubinfeld Boston data 506 census tracts, 14 variables. Pace and Barry's 1980 election data 3,107 observations 4 variables. 1st order contiguity matrix for Pace and Berry data in MATLAB sparse matrix format, 3 columns. Toledo housing values 98 census tracts average housing values and characteristics. 88 Ohio counties socio-economic variables 10 variables for 88 counties. 88 Ohio counties socio-economic variables 10 more variables for 88 counties. 88 Ohio counties socio-economic variables 10 more variables for 88 counties. Ohio 88 county x-y coordinates 88 rows by 2 columns. Documentation for Ohio data set. Data for 610 Ohio school districts Proficiency test scores, socio-economic and school characteristics. BACK to Spatial Econometrics ... http://www.rri.wvu.edu/WebBook/LeSage/spatial/data.html |
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| 5: | Index for Spatial Data Sets Spatial econometrics data sets Anselin' Columbus neighborhood crime 49 observations 3 variables plus x-y coordinates 1st order contiguity matrix for Columbus data a 49 x 49 matrix (row standardized) Harrison-Rubinfeld Boston data 506 census tracts, 14 variables H-R Boston data x-y coordinates 506 x 2 columns with latittude-longitude Documentation for H-R Boston data Pace and Barry's 1980 election data 3,107 observations 4 variables plus x-y coordinates 1st order contiguity matrix for Pace and Berry data in MATLAB sparse matrix format, 3 columns Documentation for Pace and Barry data set Toledo housing values 98 census tracts average housing values and characteristics Toledo census tract x-y coordinates latittude-longitude for 99 census tracts Documentation for Toledo data set 88 Ohio counties socio-economic variables 10 variables for 88 counties 88 Ohio counties socio-economic variables 10 more variables for 88 counties 88 Ohio counties socio-economic variables 10 more ... http://www.rri.wvu.edu/... /LeSage/Trash/spatialdatasets.html |
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| 6: | Spatial Statistics Software estimating very large spatial autoregressions (e.g., one example involves 500,000 observations). The spatial software uses sparse matrix methods to compute the matrix determinants employed in the maximum likelihood estimation of the spatial autoregressions. Specifically, the software can estimate simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), mixed regressive spatially autoregressive (MRSA) estimates as well as other lattice models which are the mainstay of spatial econometrics. Version 1.1 adds maximum likelihood estimation of spatial autoregressions which uses only nearest neighbor spatial dependence. This special case results in a closed-form for the matrix determinant and also for the log-likelihood. Naturally, this special case lends itself to computational speed. For example, for 500,000 observations one can find the nearest neighbors and compute the maximum likelihood estimates in under 3.5 minutes on a Pentium III 500! Details ... http://www.spatial-statistics.com/index.htm |
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| 7: | Spatial Statistics Articles on individual houses in Baton Rouge. The combined transformations greatly improve the pattern of the residuals and reduces their magnitude. On a Pentium Pro 200 MHz PC it took under a minute to calculate the spatial log-determinant and under 10 seconds to calculate the estimated joint transformations. I plan to include this type of function in the Spatial Statistics Toolbox. Pace, Barry, Slawson, and Sirmans (forthcoming), "Simultaneous Spatial and Functional Form Transformations," Advances in Spatial Econometrics, Florax and Anselin, Editors. Using nearest neighbor spatial dependence leads to a closed form for the eigenvalues and hence the log-determinant of the spatial weight matrix. In turn, this simple result leads to a closed-form spatial maximum likelihood estimator. Hence, one can find the neighbors and compute the maximum likelihood estimates for 100,000 observations in under one minute (on a Pentium III 500 MHz machine)! Using OLS for data known a priori to exhibit ... http://www.spatial-statistics.com/manuscript_index.htm |
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| 8: | Spatial Statistics Software estimating very large spatial autoregressions (e.g., one example involves 500,000 observations). The spatial software uses sparse matrix methods to compute the matrix determinants employed in the maximum likelihood estimation of the spatial autoregressions. Specifically, the software can estimate simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), mixed regressive spatially autoregressive (MRSA) estimates as well as other lattice models which are the mainstay of spatial econometrics. Version 1.1 adds maximum likelihood estimation of spatial autoregressions which uses only nearest neighbor spatial dependence. This special case results in a closed-form for the matrix determinant and also for the log-likelihood. Naturally, this special case lends itself to computational speed. For example, for 500,000 observations one can find the nearest neighbors and compute the maximum likelihood estimates in under 3.5 minutes on a Pentium III 500! Details ... http://www.spatial-statistics.com/ |
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| 9: | EPA abstract Environment and Planning A 2000, volume 32 (8) August, pages 1481 - 1498 Spatial econometrics, economic geography, dynamics and equilibrium: a 'third way'? Bernard Fingleton Department of Land Economy, University of Cambridge, 19 Silver Street, Cambridge CB2 9EP, England; e-mail: bf100@cam.ac.uk Received 2 December 1999; in revised form 3 March 2000 Abstract. An important item of agreement between the 'new' economic geography and economic geography 'proper' is the role of increasing returns in regional economic development. This provides a focal point for the model proposed in this paper, which suggests a 'third way' somewhere between the analysis provided by these 'two' competing modes of explanation. The paper provides empirical evidence supporting the proposed model using data on manufacturing productivity growth across 178 NUTS2 regions of the European Union. The paper also includes expressions for an equilibrium implied by the fitted model and argues that this helps ... http://www.envplan.com/epa/abstracts/a32/a321481.html |
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| 10: | Spatail Analysis Workshop Program WORKSHOP ON STATUS AND TRENDS IN SPATIAL ANALYSIS Position Papers Knowledge Extraction from Spatially Referenced Databases: a Project of an Integrated Environment Natalia Andrienko German National Research Center for Information Technology, Sankt-Augustin, Germany GIS, Spatial Econometrics and Social Science Research Luc Anselin Bruton Center for Development Studies, University of Texas at Dallas Status of GIS Use in Physical Sciences Ling Bian Department of Geography, State University of New York, Buffalo Nancy E. Bockstael Department of Agricultural and Resource Economics, University of Maryland How Succesful has GIS been at making Spatial Analysis widely available to Physical and Social Scientists? Barry Boots Wilfrid Laurier University Paul Box Department of Geography and Earth Resources, Utah State University, Utah The GIS/SA Interface for Substantive Research(ers): A Critical Need Lawrence A. Brown Department of Geography, The Ohio State University, Columbus, Ohio ... http://www.ncgia.ucsb.edu/conf/sa_workshop/papers.html |
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