Bias-Corrected Estimation for Spatial Autocorrelation
AbstractThe biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other models of a similar structure. Extensive Monte Carlo results show that the new approach performs excellently in general.
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Bibliographic InfoPaper provided by Singapore Management University, School of Economics in its series Working Papers with number 12-2010.
Length: 50 pages
Date of creation: Oct 2010
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-27 (All new papers)
- NEP-ECM-2010-11-27 (Econometrics)
- NEP-ETS-2010-11-27 (Econometric Time Series)
- NEP-MIC-2010-11-27 (Microeconomics)
- NEP-SEA-2010-11-27 (South East Asia)
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