Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending

Posted: 8 Mar 2010

Date Written: January 2010

Abstract

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.

Keywords: detrending, robust regression, yield distributions, C1, D1, D2, Q1

Suggested Citation

Finger, Robert, Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending (January 2010). American Journal of Agricultural Economics, Vol. 92, Issue 1, pp. 205-211, 2010, Available at SSRN: https://ssrn.com/abstract=1565249 or http://dx.doi.org/aap021

Robert Finger (Contact Author)

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

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