Information Theoretic Estimators of the First-Order Spatial Autoregressive Model
AbstractInformation theoretic estimators for the first-order autoregressive model are considered. Extensive Monte Carlo experiments are used to compare finite sample performance of traditional and three information theoretic estimators including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood. It is found that information theoretic estimators are robust to specification of spatial autocorrelation and dominate traditional estimators in finite samples. Finally, the proposed estimators are applied to an illustrative example of hedonic housing pricing.
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Bibliographic InfoPaper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin with number 49491.
Date of creation: 2009
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information theoretic estimators; the first-order spatial autoregressive model; Research Methods/ Statistical Methods;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-16 (All new papers)
- NEP-ECM-2009-05-16 (Econometrics)
- NEP-URE-2009-05-16 (Urban & Real Estate Economics)
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