rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Spatial and spatiotemporal econometrics
  name:
    name part: LeSage, James P.
  name:
    name part: Pace, R. Kelley.
  name:
    name part: Emerald Publishing Group
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2004
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (vi, 331 pages).
  abstract: 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, maximum likelihood, Bayesian and hierarchical Bayes, instrumental variables, generalized method of moments, maximum entropy, non-parametric and spatiotemporal. An overview of spatial econometric models and methods is provided that places contributions to this volume in the context of existing literature. New methods for estimation and inference are introduced in this volume and Monte Carlo comparisons of existing methods are described. In addition to topics involving estimation and inference, approaches to model comparison and selection are set forth along with new tests for spatial dependence and functional form. These methods are applied to a variety of economic problems including: hedonic real estate pricing, agricultural harvests and disaster payments, voting behaviour, identification of edge cities, and regional labour markets. The volume is supported by a web site containing data sets and software to implement many of the methods described by contributors to this volume.
  table of contents: 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.
  note: edited by James P. Lesage and R. Kelley Pace.
  note: Electronic resource.
  subject:
    topic: Business & Economics
    topic: Econometrics
  subject:
    topic: Business & Economics
    topic: Economics
    topic: Macroeconomics
  subject:
    topic: Econometrics
  subject:
    topic: Econometrics
  classification: HB139 .S63 2004
  classification: 330.015195
  classification: 330.43
  related item:
    title info:
      title: Advances in econometrics ; v. 18
  identifier: 9781849503013 (electronic bk.) :
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UtOrBLW
    record creation date: 101115
    record change date: 20190329194111.0
    record identifier: in00002700735