Income Diffusion and Spatial Econometric Models

This paper considers the development of linked small-area spatial econometric models in which income flows between the areas constitute an important part of model specification. The attributes of income diffusion processes are considered in detail. Both static models and temporal income diffusion models are discussed. The spatial and spatial-temporal autocorrelation functions are derived which provide a measure of the spatial distribution of income. The Green function is also derived as a measure of income impulses through the system of regions. An important aspect of the paper is to relate properties of these functions to key economic parameters in particular propensities to spend locally and to spend nonlocally.     

Statistical Tests and Process Generators For Random Field Models

Two alternative specifications for random fields are reviewed and models from both specifications identified. The paper then deals with statistical tests for discriminating between randomness and some of the models of dependence. These various tests are compared and criteria established for their use. Additional testing procedures are identified for the more difficult problem of distinguishing between models of dependence. The paper then looks at different process generators for certain random field models and concludes with a discussion of the importance of the theory for handling diffusion processes. The value of these results for both model specification and “form-process” studies is emphasized.     

Analyzing the Relationship Between Smoking and Coronary Heart Disease at the Small Area Level: A Bayesian Approach to Spatial Modeling

We model the relationship between coronary heart disease and smoking prevalence and deprivation at the small area level using the Poisson log-linear model with and without random effects. Extra-Poisson variability (overdispersion) is handled through the addition of spatially structured and unstructured random effects in a Bayesian framework. In addition, four different measures of smoking prevalence are assessed because the smoking data are obtained from a survey that resulted in quite large differences in the size of the sample across the census tracts. Two of the methods use Bayes adjustments of standardized smoking ratios (local and global adjustments), and one uses a nonparametric spatial averaging technique. A preferred model is identified based on the deviance information criterion. Both smoking and deprivation are found to be statistically significant risk factors, but the effect of the smoking variable is reduced once the confounding effects of deprivation are taken into account. Maps of the spatial variability in relative risk, and the importance of the underlying covariates and random effects terms, are produced. We also identify areas with excess relative risk.     

DIAGNOSTICS FOR REGRESSION MODELING IN SPATIAL ECONOMETRICS*

ABSTRACT. This paper describes statistics for model criticism in spatial econometrics. The purpose of these statistics is to evaluate how well a chosen model fits the data and to identify influential cases and how they affect the aggregate picture. The paper reviews results in Martin (1992) for the regression model with correlated errors where the coefficients of the variance matrix are assumed either known or fixed. The problems of applying the statistics in spatial econometric modeling are discussed. An application is reported which considers diagnostics for the mean function and highlights cases that might influence estimates of the parameter of the error model. Different ways of assessing the influence of cases are also described.     

海宁郜家岭良渚文化墓地发掘报告

海宁郜家岭良渚化墓地是一处良渚化人工堆筑营建的级别较低的贵族阶层墓地,为了解良渚化的社会结构提供了可贵的新资料。     

Model Specification in Stationary Random Fields

The problems of model specification in one- and two-dimensional stationary random fields are considered, with particular reference to the class of spatial autoregressions. The role of the autocorrelation function is discussed and it is shown to have a valuable exploratory use, especially in identifying directional bias in random fields. It is recommended that this analysis be supplemented by a likelihood-ratio test, which permits more precise tests of hypotheses about the form of the underlying model.