Spatial Econometrics: Methods and ModelsSpatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis. |
Contents
THE SCOPE OF SPATIAL ECONOMETRICS | 7 |
THE FORMAL EXPRESSION OF SPATIAL EFFECTS | 16 |
A TYPOLOGY OF SPATIAL ECONOMETRIC MODELS | 32 |
SPATIAL STOCHASTIC PROCESSES TERMINOLOGY | 41 |
THE MAXIMUM LIKELIHOOD APPROACH | 57 |
Some Useful Results on Matrix Calculus | 74 |
ALTERNATIVE APPROACHES TO INFERENCE | 81 |
SPATIAL DEPENDENCE IN REGRESSION | 100 |
First Order Conditions for the ML Estimator in the Spatial | 157 |
ML Estimator in the TwoEquation Spatial | 161 |
PROBLEM AREAS IN ESTIMATION AND TESTING | 169 |
OPERATIONAL ISSUES AND EMPIRICAL | 180 |
A Bisection Search for the Coefficient in a Pure Spatial | 216 |
MODEL VALIDATION AND SPECIFICATION TESTS | 223 |
MODEL SELECTION IN SPATIAL ECONOMETRIC | 243 |
CONCLUSIONS | 253 |
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Common terms and phrases
alternative Anselin applied approach asymptotic Bayesian Chapter considered consists constraints context correlation covariance matrix data set derivation discussed distribution empirical equation error component error covariance error term error variance estimators and tests explanatory variables expressed finite sample formal framework function heteroskedasticity implementation information matrix instrumental variables issue iteration Journal of Econometrics lagged dependent variable Lagrange Multiplier likelihood function linear regression LM test log-likelihood maximum likelihood estimation misspecification ML estimation model with spatially Moran non-nested nonlinear null hypothesis observations parameters presence of spatial problem properties random variables regional science relevant Section Seemingly Unrelated Regressions significance situation space-time spatial analysis spatial autocorrelation spatial autoregressive model spatial dependence spatial econometrics spatial effects spatial heterogeneity spatial models spatial process models spatial units spatial weight matrix spatially lagged dependent specification tests stochastic process techniques tests for spatial variance matrix vector Wald Wald test Χβ