Evaluating robust regression techniques for detrending crop yield data with nonnormal errors

Scott M. Swinton, Robert P King

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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 com yields. The evaluation covers actual farm-level com 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.

Original languageEnglish (US)
Pages (from-to)446-451
Number of pages6
JournalAmerican Journal of Agricultural Economics
Volume73
Issue number2
DOIs
StatePublished - May 1991

Keywords

  • Detrending
  • Regression diagnostics
  • Robust estimation
  • Yield distributions

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