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 Econometrics
    sub title: Statistical Foundations and Applications to Regional Convergence
  name:
    name part: Arbia, Giuseppe.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 207 p. 19 illus. online resource.
  abstract: In recent years the so-called new economic geography and the issue of regional economic convergence have increasingly drawn the interest of economists to the empirical analysis of regional and spatial data. However, even if the methodology for econometric treatment of spatial data is well developed, there does not exist a textbook theoretically grounded, well motivated and easily accessible to eco- mists who are not specialists. Spatial econometric techniques receive little or no attention in the major econometric textbooks. Very occasionally the standard econometric textbooks devote a few paragraphs to the subject, but most of them simply ignore the subject. On the other hand spatial econometric books (such as Anselin, 1988 or Anselin, Florax and Rey, 2004) provide comprehensive and - haustive treatments of the topic, but are not always easily accessible for people whose main degree is not in quantitative economics or statistics. This book aims at bridging the gap between economic theory and spatial stat- tical methods. It starts by strongly motivating the reader towards the problem with examples based on real data, then provides a rigorous treatment, founded on s- chastic fields theory, of the basic spatial linear model, and finally discusses the simpler cases of violation of the classical regression assumptions that occur when dealing with spatial data.
  table of contents: Motivation -- Random Fields and Spatial Models -- Likelihood Function for Spatial Samples -- The Linear Regression Model with Spatial Data -- Italian and European ?-convergence Models Revisited -- Looking Ahead: A Review of More Advanced Topics in Spatial Econometrics.
  note: by Giuseppe Arbia.
  subject:
    topic: Geographical information systems
  subject:
    topic: Statistics
  subject:
    topic: Economic theory
  subject:
    topic: Econometrics
  subject:
    topic: Regional economics
  subject:
    topic: Spatial economics
  subject:
    topic: Economics
  subject:
    topic: Econometrics
  subject:
    topic: Economic Theory/Quantitative Economics/Mathematical Methods
  subject:
    topic: Regional/Spatial Science
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: Geographical Information Systems/Cartography
  classification: HB139-141
  classification: 330.015195
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Spatial Science
  identifier: 9783540323051
  identifier: http://dx.doi.org/10.1007/3-540-32305-8
  record info:
    record creation date: 100301
    record change date: 20151204172108.0
    record identifier: 978-3-540-32305-1