Spatial Econometrics: Statistical Foundations and Applications to Regional Convergence
In recent years the so-called new economic geography and the issue of regional economic convergence have increasingly drawn the interest of economists to the empirical analysis of regional and spatial data. However, even if the methodology for econometric treatment of spatial data is well developed, there does not exist a textbook theoretically grounded, well motivated and easily accessible to eco- mists who are not specialists. Spatial econometric techniques receive little or no attention in the major econometric textbooks. Very occasionally the standard econometric textbooks devote a few paragraphs to the subject, but most of them simply ignore the subject. On the other hand spatial econometric books (such as Anselin, 1988 or Anselin, Florax and Rey, 2004) provide comprehensive and - haustive treatments of the topic, but are not always easily accessible for people whose main degree is not in quantitative economics or statistics. This book aims at bridging the gap between economic theory and spatial stat- tical methods. It starts by strongly motivating the reader towards the problem with examples based on real data, then provides a rigorous treatment, founded on s- chastic fields theory, of the basic spatial linear model, and finally discusses the simpler cases of violation of the classical regression assumptions that occur when dealing with spatial data.
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92 Italian provinces alternative hypothesis Anselin Arbia assume assumption asymptotic Barro and Sala-i-Martin Besag Chapter connectivity matrix consider context Convergence of per-capita defined derive distribution econometric literature economic empirical Equation European NUTS-2 regions expected value expressed Gaussian geographical growth rates Half-life heteroskedasticity homoskedasticity hypothesis testing independence introduced ISBN joint probability density Lagrange multiplier Least Squares LeSage likelihood function linear regression linear regression model log-likelihood maximum likelihood estimators neighbours non-systematic component normality null hypothesis observations obtain panel data parameters per-capita GDP per-capita income probability density function probability model problem quartile random field X(s random variables refer regression model represents residuals sampling model Section space spatial autocorrelation spatial correlation spatial data spatial dependence spatial econometric spatial error model spatial lag model spatial model specification speed of convergence techniques test statistics variance variance-covariance matrix vector white noise
Problemas del desarrollo, Issue 149
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Collecting Spatial Data: Optimum Design of Experiments for Random Fields
Werner G. Müller
No preview available - 2007