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Computing the Jacobian in spatial models: an applied survey

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  • Bivand, Roger

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    (Dept. of Economics, Norwegian School of Economics and Business Administration)

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    Abstract

    Despite attempts to get around the Jacobian in fitting spatial econometric models by using GMM and other approximations, it remains a central problem for maximum likelihood estimation. In principle, and for smaller data sets, the use of the eigenvalues of the spatial weights matrix provides a very rapid and satisfactory resolution. For somewhat larger problems, including those induced in spatial panel and dyadic (network) problems, solving the eigenproblem is not as attractive, and a number of alternatives have been proposed. This paper will survey chosen alternatives, and comment on their relative usefulness.

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    Bibliographic Info

    Paper provided by Department of Economics, Norwegian School of Economics in its series Discussion Paper Series in Economics with number 20/2010.

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    Length: 30 pages
    Date of creation: 17 Aug 2010
    Date of revision:
    Handle: RePEc:hhs:nhheco:2010_020

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    Related research

    Keywords: Spatial autoregression; Maximum likelihood estimation; Jacobian computation; Econometric software.;

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    Cited by:
    1. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.

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