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

ONE PARTY TO AN EXCHANGE often knows something relevant to the transaction that the other party does not know. Such asymmetries of information are pervasive in economic activity: for example, in the relationship between employer and employee when the employee's effort cannot be monitored perfectly; between the stockholders and the manager of a firm; between insurer and insured; between a regulated firm and the regulatory agency; between the supplier and the consumers of a public good; between a socialist firm and the central planner; or (as is the subject of this paper) between buyer and seller when the value of the item is uncertain. Forty years ago, F. A. Hayek criticized theories that purport to describe the price system but start from the assumption that individuals have symmetric information: The peculiar character of the problem of a rational economic order is determined precisely by the fact that the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess. The economic problem of society is thus not merely a problem of how to allocate "given" resources-if "given" is taken to mean given to a single mind which deliberately solves the problem set by these "data." It is rather a problem of how to secure the best use of resources known to any of the members of society, for ends whose relative importance only these individuals know. Or, to put it briefly, it is a problem of the utilization of knowledge which is not given to anyone in its totality. (Hayek 1945, p. 519)

Auctions and Bidding

The authors used theoretical models to organize the diverse unemployment literature, and meta-analytic techniques were used to examine the impact of unemployment on worker well-being across 104 empirical studies with 437 effect sizes. Unemployed individuals had lower psychological and physical well-being than did their employed counterparts. Unemployment duration and sample type (school leaver vs. mature unemployed) moderated the relationship between mental health and unemployment, but the current unemployment rate and the amount of unemployment benefits did not. Within unemployed samples, work-role centrality, coping resources (personal, social, financial, and time structure), cognitive appraisals, and coping strategies displayed stronger relationships with mental health than did human capital or demographic variables. The authors identify gaps in the literature and propose directions for future unemployment research.

Psychological and physical well-being during unemployment: a meta-analytic study.

Using the well-known Harrison and Rubinfeld (1978) hedonic pricing data, this manuscript demonstrates the substantial benefits obtained by modeling the spatial dependence of the errors. Specifically, the estimated errors on the spatial autoregression fell by 44% relative to OLS. The spatial autoregression corrects predicted values by a nonparametric estimate of the error on nearby observations and thus mimics the behavior of appraisers. The spatial autoregression, by formally incorporating the areal configuration of the data to increase predictive accuracy and estimation efficiency, has great potential in real estate empirical work.

Using the Spatial Configuration of the Data to Improve Estimation

A method for spatial–temporal forecasting with an application to real estate prices

On the Harrison and Rubinfeld Data

One of the major unsettled questions in the study of competitive bidding concerns the impact of additional competition in auctions on the optimal bid levels of competing firms. Numerous theoretical and simulation studies suggest an inverse relationship between the expected number of competitors and the bid level of a particular firm in sealed bid auctions involving objects of uncertain value. The few empirical studies that have been done contradict this assertion. In this article, we address this issue by pointing out a serious statistical defect in previous empirical work and by reestimating a bid level equation by using a more appropriate technique. New evidence is provided which reconciles the differences between previous empirical results and the major predictions of the widely accepted bidding theory models. The new results presented here support the conclusions of the theoretical studies.

The Competitive Effect in Bonus Bidding: New Evidence

The vast majority of market valuations employ either some formal estimator such as ordinary least squares (OLS) or rely upon an informal set of rules defining the grid adjustment estimator. The success of the grid adjustment estimator suggests the data do not obey the ideal assumptions underlying OLS. However, the grid adjustment estimator's lack of a formal statistical foundation makes it difficult to use for inference and other purposes. This article demonstrates how to generalize the grid estimator and OLS to potentially obtain the best features of both. Interestingly, the generalization defines a spatial autoregression. On an empirical example the spatial autoregression outperforms the grid estimator which in turn outperforms OLS.

Otis W. Gilley

Papers

Using the Spatial Configuration of the Data to Improve Estimation

A method for spatial–temporal forecasting with an application to real estate prices

On the Harrison and Rubinfeld Data

The Competitive Effect in Bonus Bidding: New Evidence

Generalizing the OLS and Grid Estimators