Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending
Robert Finger
American Journal of Agricultural Economics, 2010, vol. 92, issue 1, 205-211
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. Copyright 2010, Oxford University Press.
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)
Downloads: (external link)
http://hdl.handle.net/10.1093/ajae/aap021 (application/pdf)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:92:y:2010:i:1:p:205-211
Access Statistics for this article
American Journal of Agricultural Economics is currently edited by Madhu Khanna, Brian E. Roe, James Vercammen and JunJie Wu
More articles in American Journal of Agricultural Economics from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().