Spatial and spatiotemporal econometrics / edited by James P. LeSage, R. Kelley Pace.

Includes bibliographical references and index.

Introduction / James P. LeSage, R. Kelley Pace -- Bayesian model choice in spatial econometrics / Leslie W. Hepple -- A Bayesian probit model with spatial dependencies / Tony E. Smith, James P. LeSage -- Instrumental variable estimation of a spatial autoregressive model with autoregressive disturbances : large and small sample results / Harry H. Kelejian, Ingmar R. Prucha, Yevgeny Yuzefovich -- Generalized maximum entropy estimation of a first order spatial autoregressive model / Thomas L. Marsh, Ron C. Mittelhammer -- Employment subcenters and home price appreciation rates in metropolitan Chicago / Daniel P. McMillen -- Searching for housing submarkets using mixtures of linear models / M.D. Ugarte, T. Goicoa, A.F. Militino -- Spatio-temporal autoregressive models for U.S. unemployment rate / Xavier de Luna, Marc G. Genton -- A learning rule for inferring local distributions over space and time / Stephen M. Stohs, Jeffrey T. LaFrance -- Testing for linear and log-linear models against box-cox alternatives with spatial lag dependence / Badi H. Baltagi, Dong Li -- Spatial lags and spatial errors revisited : some Monte Carlo evidence / Robin Dubin.

This volume focuses on econometric models that confront estimation and inference issues occurring when sample data exhibit spatial or spatiotemporal dependence. This can arise when decisions or transactions of economic agents are related to the behaviour of nearby agents. Dependence of one observation on neighbouring observations violates the typical assumption of independence made in regression analysis. Contributions to this volume by leading experts in the field of spatial econometrics provide details regarding estimation and inference based on a variety of econometric methods including, ma.

Print version record.

English.

eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - Worldwide