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- W2094329897 abstract "Abstract When using maximum likelihood estimation for spatial models, a well known problem is the computation of the logarithm of the determinant of the Jacobian, especially for problems with a huge number of observation units. In the recent literature there are various promising approaches to account for these numerical difficulties, relying on alternative decompositions or approximations. Recently, a general method of moments approach for estimating these models was developed. We compare all these different approaches with respect to their root mean-squared errors of the estimates and investigate the size and power of hypotheses tests with respect to the spatial correlation and the regression parameters." @default.
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- W2094329897 date "2008-02-01" @default.
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- W2094329897 title "Performance contest between MLE and GMM for huge spatial autoregressive models" @default.
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- W2094329897 doi "https://doi.org/10.1080/10629360600954109" @default.
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