计量经济分析 = Econometric analysis

Statistics for Spatial Data.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

A space-time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects This general specification encompasses several more specific space-time structures that have been used recently in the panel data literature Markov Chain Monte Carlo methods are set forth for estimating the model which allow simple treatment of initial period observations as endogenous or exogenous Performance of the approach is demonstrated using both Monte Carlo experiments and an appliedillustration

A Space-Time Filter for Panel Data Models Containing Random Effects

A spatial Hausman test

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labelled MC to the third by Madigan and York (1995) is developed for two types of spatial econometric models that are frequently used in the literature. The methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough that calculation of posterior probabilities for all models is difficult or infeasible. Estimates and inferences are produced by averaging over models using the posterior model probabilities as weights, a procedure known as Bayesian model averaging. We illustrate the methods using a spatial econometric model of origin-destination population migration flows between the 48 US States and District of Columbia during the 1990 to 2000 period.

Bayesian Model Averaging for Spatial Econometric Models

Numerous authors have suggested that omitted variables affect spatial regression methods less than ordinary least-squares (OLS; Dubin 1988; Brasington and Hite 2005, Cressie 1993). To explore these conjectures, we derive an expression for OLS omitted variable bias in a univariate model with spatial dependence in the disturbances and explanatory variables. There are a number of motivations for making this set of assumptions regarding the disturbances and explanatory variables. First, in spatial regression models each observation represents a region or point located in space, for example, census tracts, counties or individual houses. Sample data used as explanatory variables in these models typically consists of socioeconomic, census and other characteristics of the regional or point locations associated with each observation. Therefore, spatial dependence in the explanatory variables seems likely, motivating our choice of this assumption. Note, the literature rarely examines the spatial character of the explanatory variables, but this can affect the relative performance of OLS as shown below. Second, application of OLS models to regional data samples frequently leads to spatial dependence in the regression disturbances, providing a justification for this assumption. Finally, there are a host of latent unobservable and frequently unmeasurable influences that are likely to impact spatial regression relationships. For example, factors such as location and other types of amenities, highway accessibility, school quality or neighborhood prestige may exert an influence on the dependent variable in hedonic house price models. It is unlikely that explanatory variables are readily available to capture all of these types of latent influences. This type of reasoning motivates our focus on the impact of omitted explanatory variables. Since the omitted and included explanatory variables are both likely to exhibit spatial dependence based on the same spatial connectivity structure, it seems likely that omitted and included variables will exhibit

https://www.springer.com/cda/content/document/cda_downloaddocument/9783642033247-c1.pdf?SGWID=0-0-45-822126-p173916775

Omitted Variable Biases of OLS and Spatial Lag Models

Spatial interaction or gravity models have been used to model flows that take many forms, for example population migration, commodity flows, traffic flows, all of which reflect movements between origin and destination regions. We focus on how to interpret estimates from spatial autoregressive extensions to the conventional regression-based gravity models that relax the assumption of independence between flows. These models proposed by LeSage and Pace (2008, 2009) define spatial dependence involving flows between regions. We show how to calculate partial derivative expressions for these models that can be used to quantify these various types of effect that arise from changes in the characteristics/explanatory variables of the model.

https://papers.ssrn.com/sol3/Delivery.cfm/JORS_JORS12114.pdf?abstractid=2573374&mirid=1&type=2

James P. LeSage

Papers

A Space-Time Filter for Panel Data Models Containing Random Effects

A spatial Hausman test

Bayesian Model Averaging for Spatial Econometric Models

Omitted Variable Biases of OLS and Spatial Lag Models

Interpreting spatial econometric origin-destination flow models