Computing the Jacobian in spatial models: an applied survey

Roger Bivand

No 20/2010, Discussion Paper Series in Economics from Norwegian School of Economics, Department of Economics

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.

Keywords: Spatial autoregression; Maximum likelihood estimation; Jacobian computation; Econometric software. (search for similar items in EconPapers)
JEL-codes: C13 C21 C87 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2010-08-17
New Economics Papers: this item is included in nep-ecm and nep-ure
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