Computing the Jacobian in spatial models: an applied survey
AbstractDespite 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 InfoPaper provided by Department of Economics, Norwegian School of Economics in its series Discussion Paper Series in Economics with number 20/2010.
Length: 30 pages
Date of creation: 17 Aug 2010
Date of revision:
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Spatial autoregression; Maximum likelihood estimation; Jacobian computation; Econometric software.;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
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- NEP-ECM-2011-05-30 (Econometrics)
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