Spatial Econometrics: Statistical Foundations and Applications to Regional ConvergenceSpringer Science & Business Media, 2006 M06 8 - 207 páginas 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. |
Contenido
Motivation | 3 |
Random Fields and Spatial Models | 31 |
Likelihood Function for Spatial | 73 |
The Linear Regression Model with Spatial Data | 85 |
Italian and European βconvergence Models Revisited | 135 |
A Review of More Advanced Topics | 147 |
A Review of the Available Software for Spatial | 163 |
List of Tables 191 | 190 |
Términos y frases comunes
92 Italian provinces alternative hypothesis Anselin applied Arbia G assume assumption asymptotic Besag conditional consider covariance defined Definition derive distribution econometric analysis economic empirical Equation European regions expected value expressed Gaussian geographical growth rates Half-life heteroskedasticity homoskedasticity hypothesis testing independence introduced ISBN Kelejian Lagrange multiplier LeSage likelihood function linear regression linear regression model log-likelihood Markov maximum likelihood estimates neighbours non-systematic component normality null hypothesis observations obtain Paelinck panel data parameters per-capita GDP per-capita income Pinkse probability density function probability model problem procedures quartile random field X(s random variables Regional Science residuals sampling model Section space spatial autocorrelation spatial autoregressive spatial correlation spatial data spatial dependence spatial econometric spatial error model spatial lag model spatial model specification speed of convergence techniques test statistics tests for spatial tion variance variance-covariance matrix vector white noise