Abstract
Spatial regression models allow us to account for dependence among observations, which often arises when observations are collected from points or regions located in space. The spatial sample of observations being analyzed could come from a number of sources. Examples of point-level observations would be individual homes, firms, or schools. Regional observations could reflect average regional household income, total employment or population levels, tax rates, and soon. Regions often have widely varying spatial scales (for example, European Unionregions, countries, or administrative regions such as postal zones or census tracts).
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Anselin L (1988) Spatial econometrics: Methods and models. Kluwer, Dordrecht
Anselin L (2006) GeoDa™ 0.9 User's guide, at geoda.uiuc.edsu
Barry R, Pace RK (1999) A Monte Carlo estimator of the log determinant of large sparse matrices. Lin Algebra Appl 289(1–3):41–54
Bivand R (2002) Spatial econometrics functions in R: classes and methods. J Geogr Syst 4(4):405–421
Casetti E (1997) The expansion method, mathematical modeling, and spatial econometrics. Int Reg Sci Rev 20(1–2):9–33
Casetti E (2009) Expansion method, dependency, and modeling. In Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp. 487–505
Chen J, Jennrich R (1996) The signed root deviance profile and confidence intervals in maximum likelihood analysis. J Am Stat Assoc 91:993–998
Dall'erba S, LeGallo J (2007) Regional convergence and the impact of European structural funds over 1989–1999: a spatial econometric analysis. Papers in Reg Sci 87(2):219–244
Ertur C, Koch W (2007) Convergence, human capital and international spillovers. J Appl Econ 22(6):1033–1062
Fischer MM, Bartkowska M, Riedl A, Sardadvar S, Kunnert A (2009) The impact of human capital on regional labor productivity growth in Europe. In Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp. 585–597
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New York, Chichester, Toronto and Brisbane
Gelfand AE, Smith AFM (1990) Sampling-based approaches to calculating marginal densities. J Am Stat Assoc 85:398–409
Gelfand AE, Hills SE, Racine-Poon A, Smith AFM (1990) Illustration of Bayesian inference in normal data models using Gibbs sampling. J Am Stat Assoc 85:972–985
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transact Patt Anal Machine Intell 6(6):721–741
Hastings WK (1970) Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 57(1):97–109
Kelejian H, Prucha IR (1998) A Generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. J Real Est Fin Econ 17(1):99–121.
Kelejian H, Prucha IR (1999) A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev 40(2):509–533
Kelejian H, Tavlas HGS, Hondronyiannis G (2006) A spatial modeling approach to contagion among emerging economies. Open Econ5 Rev5 17(4/5):423–442
Koop G (2003) Bayesian econometrics. Wiley, West Sussex [UK]
Lee LF (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial econometric models. Econometrica 72(6):1899–1926
LeSage JP (1997) Bayesian estimation of spatial autoregressive models. Int Reg Sci Rev 20(1–2):113–129
LeSage JP (1999) Spatial econometrics using MATLAB a manual for the spatial econometrics toolbox functions, available at www.spatial-econometrics.com
LeSage JP, Fischer MM (2008) Spatial growth regressions: model specification, estimation and interpretation. Spat Econ Anal 3(3):275–304
LeSage JP, Fischer MM (2009) Spatial econometric methods for modeling origin-destination flows. In Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp. 409–433
LeSage JP, Pace RK (2007) A matrix exponential spatial specification. J Econometrics 140(1):190–214
LeSage JP, Pace RK (2008) Spatial econometric modeling of origin-destination flows. J Reg Sci 48(5):941–967
LeSage JP, Pace RK (2009) Introduction to spatial econometrics. CRC Press (Taylor and Francis Group). Boca Raton [FL], London and New York
Marsh TL, Mittelhammer RC (2004) Generalized maximum entropy estimation of a first order spatial autoregressive model. In LeSage JP, Pace RK (eds) Advances in econometrics, volume 18. Elsevier, Oxford, pp. 203–238
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Physics 21(6):1087–1092
Ord JK (1975) Estimation methods for models of spatial interaction. J Am Stat Assoc 70(349):120–126
Pace RK (2003) Matlab spatial statistics toolbox 2.0, available at www.spatial-statistics.com.
Pace RK, Barry B (1997) Quick computation of spatial autoregressive estimators. Geogr Anal 29(3):232–246
Pace RK, LeSage JP (2003) Likelihood dominance spatial inference. Geogr Anal 35(2):133–147
Pace RK, LeSage JP (2009a) Omitted variables biases of OLS and spatial lag models. In Páez A, LeGallo J, Buliung R, Dall'Erba S (eds) Progress in spatial analysis: theory and methods, and thematic applications. Springer, Berlin, Heidelberg and New York (forthcoming)
Pace K, LeSage JP (2009b) A sampling approach to estimating the log determinant used in spatial likelihood problems. J Geogr Syst (forthcoming)
Parent O, LeSage JP (2009) Spatial econometric model averaging. In Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp. 435–460
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. Comput Stat Data Anal 53(8):2980–2988
Wheeler DC, Páez A (2009) Geographically weighted regression. 1er MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin, Heidelberg and New York, pp. 465–486
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
LeSage, J.P., Pace, R.K. (2010). Spatial Econometric Models. In: Fischer, M., Getis, A. (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03647-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-03647-7_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03646-0
Online ISBN: 978-3-642-03647-7
eBook Packages: Business and EconomicsEconomics and Finance (R0)