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Evaluating Robust Regression Techniques for Detrending Crop Yield Data with Nonnormal Errors

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  • Scott M. Swinton
  • Robert P. King

Abstract

Although ordinary least squares is not efficient when errors are not distributed normally, it generates better crop yield trend coefficient estimates than six alternative robust regression methods. This is because of the econometric properties of an uninterrupted series independent variable as well as the level of skewness typical of corn yields. The evaluation covers actual farm-level corn yield series as well as a set of "contaminated" data series and one thousand sets of Monte Carlo yield series. Where an influential end-of-series outlier is suspected, the DFBETAS regression diagnostic statistic is recommended.

Suggested Citation

  • Scott M. Swinton & Robert P. King, 1991. "Evaluating Robust Regression Techniques for Detrending Crop Yield Data with Nonnormal Errors," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 446-451.
    • Handle: RePEc:oup:ajagec:v:73:y:1991:i:2:p:446-451.
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    16. Boyer, Christopher M. & Lambert, Dayton M. & Larson, James A. & Tyler, Donald, 2017. "Investment Analysis of Long-term Cover Crops and Tillage Systems on Cotton Production," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258525, Agricultural and Applied Economics Association.
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