James P. LeSage

Bio: James P. LeSage is an academic researcher from Texas State University. The author has contributed to research in topics: Spatial dependence & Spatial econometrics. The author has an hindex of 51, co-authored 219 publications receiving 12096 citations. Previous affiliations of James P. LeSage include University of Toledo & College of Business Administration.

Introduction to spatial econometrics

Abstract: An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regression models are useful when modeling cross-sectional regional observations or and panel data samples collected from regions over both space and time can be easily implemented using publicly available software. Use of these models for the case of non-spatial structured dependence is also discussed.

Spatial econometric modeling of origin-destination flows*

Abstract: Standard spatial autoregressive models rely on spatial weight structures constructed to model dependence among n regions. Ways of parsimoniously modeling the connectivity among the sample of N=n2 origin-destination (OD) pairs that arise in a closed system of interregional flows has remained a stumbling block. We overcome this problem by proposing spatial weight structures that model dependence among the N OD pairs in a fashion consistent with standard spatial autoregressive models. This results in a family of spatial OD models introduced here that represent an extension of the spatial regression models described in Anselin (1988).

The Theory and Practice of Spatial Econometrics

Abstract: This text provides an introduction to spatial econometric theory along withnumerous applied illustrations of the models and methods described. The ap-plications utilize a set of MATLAB functions that implement a host of spatialeconometric estimation methods. The intended audience is faculty,students andpractitioners involved in modeling spatial data sets. The MATLAB functionsdescribed in this book have been used in my own research as well as teach-ing both undergraduate and graduate econometrics courses. They are availableon the Internet at http://www.econ.utoledo.edu along with the data sets andexamples from the text.The theory and applied illustrations of conventional spatial econometricmodels represent about half of the content in this text,with the other halfdevoted to Bayesian alternatives. Conventional maximum likelihood estimationfor a class of spatial econometric models is discussed in one chapter,followed bya chapter that introduces a Bayesian approach for this same set of models. Itis well-known that Bayesian methods implemented with a diffuse prior simplyreproduce maximum likelihood results,and we illustrate this point. However,the main motivation for introducing Bayesian methods is to extend the conven-tional models. Comparative illustrations demonstrate how Bayesian methodscan solve problems that confront the conventional models. Recent advances inBayesian estimation that rely on Markov Chain Monte Carlo (MCMC) methodsmake it easy to estimate these models. This approach to estimation has beenimplemented in the spatial econometric function library described in the text,so estimation using the Bayesian models require a single additional line in yourcomputer program.Some of the Bayesian methods have been introduced in the regional scienceliterature,or presented at conferences. Space and time constraints prohibit anydiscussion of implementation details in these forums. This text describes the im-plementation details,which I believe greatly enhance understanding and allowusers to make intelligent use of these methods in applied settings. Audienceshave been amazed (and perhaps skeptical) when I tell them it takes only 10seconds to generate a sample of 1,000 MCMC draws from a sequence of condi-tional distributions needed to estimate the Bayesian models. Implementationapproaches that achieve this type of speed are described here in the hope thatother researchers can apply these ideas in their own work.I have often been asked about Monte Carlo evidence for Bayesian spatiali

Language functional magnetic resonance imaging in preoperative assessment of language areas: correlation with direct cortical stimulation.

Abstract: OBJECTIVE: The aim of this study was to analyze the usefulness of preoperative language lunctional magnetic resonance imaging (IMRI), by correlating (MR) data with intraoperative cortical stimulation results for patients with brain tumors. METHODS: Naming and verb generation tasks were used, separately or in combination, for 14 right-handed patients with tumors in the left hemisphere, (MR) data obtained were analyzed with SPM soitware, with two standact analysis thresholds (P < 0.005 and then P < 0.05). The (MR) data were then registered in a frameless stereotactic neuronavigational device and correlated with direct brain mapping results. We used a statistical model with the (MR) information as a predictor, spatially correlating each intraoperatively mapped cortical site with (MR) data integrated in the neuronavigational system (site-by-site correlation). Fight patients were also studied with language (MR) postoperatively, with the same acquisition protocol. RESULTS: We observed high variability in signal extents and locations among patients with both tasks The activated arcas were located mainly in the left hemisphere in the middle and inferior frontal gyri (F2 and F3), the superior and middle temporal gyri (T1 and T2), and the supramarginal and angular gyri. A total of 426 cortical sites were tested for each task among the 14 patients. In frontal and temporoparietal areas, poor sensitivity of the (MR) technique was observed for the naming and verts generation takss (22 and 36%, respectively) with P < 0.005 as the analysis threshold. Although not perfect, the specificity of the (MR) technique was good in all conditions (97% for the naming task and 98% for the verb generation task). Better correlation (sensitivity, 59%; specificity, 97%) was achieved by combining the two (MR) tasks. Variation of the analysis threshold to P < 0.05 increased the sensitivity to 66% while decreasing the speciticity to 91 %. Postoperative (MR) data (for the cortical brain areas studied intraoperatively) werein accordance with brain mapping results for six of eight patients. Complete agreement between pre- and postoperative (MR) studies and direct brain mapping results was observed for only three of eight patients. CONCLUSION: With the paradigms and analysis thresholds used in this study, language (MR) data obtained with naming or verb generation tasks, before and after surgery, were imperfectly correlated with intraoperative brain mapping results. A better correlation could be obtained by combining the (MR) tasks. The overall results of this study demonstrated that language (MR) could not be used to make critical surgical decisions in the absence of direct brain mapping. Other acquisition protocols are required for evaluation of the potential role of language (MR) in the accurate detection of essential cortical language areas.

Spatial Growth Regressions: Model Specification, Estimation and Interpretation

Abstract: This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feedback nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 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.

Estimation and Inference in Econometrics

Abstract: 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.

Economics of Information

Abstract: 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.