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.