Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending
Using a Monte Carlo experiment, the performance of the ordinary least squares (OLS) and the MM-estimator, a robust regression technique, is compared in an application of crop yield detrending. Assuming symmetric as well as skewed crop yield distributions, we show that the MM-estimator performs similarly to OLS for uncontaminated time series of crop yield data, and clearly outperforms OLS for outlier-contaminated samples. In contrast to earlier studies, our analysis suggests that robust regression techniques, such as the MM-estimator, should be reconsidered for detrending crop yield data. Copyright 2010, Oxford University Press.
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Volume (Year): 92 (2010)
Issue (Month): 1 ()
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- Christophe Croux & Geert Dhaene & Dirk Hoorelbeke, 2003. "Robust Standard Errors for Robust Estimators," Center for Economic Studies - Discussion papers ces0316, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
- Richard E. Just & Quinn Weninger, 1999.
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Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
- Woodard, Joshua D. & Garcia, Philip, 2008. "Weather Derivatives, Spatial Aggregation, and Systemic Risk: Implications for Reinsurance Hedging," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(01), April.
- Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
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