Efficient estimation of agricultural time series models with nonnormal dependent variables

Octavio A. Ramírez, Sukant K. Misra, Jeannie Nelson

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

This article proposes using an expanded form of the Johnson SU family as a way to approximate nonnormal distributions in regression models. The distribution is one of the few that allows modeling heteroskedasticity and autocorrelation. The technique is evaluated with Monte Carlo simulation and illustrated through an empirical model of the West Texas cotton basis. Given nonnormality, this technique can substantially reduce the variance of slope parameter estimates relative to least squares procedures.

Original languageEnglish
Pages (from-to)1029-1040
Number of pages12
JournalAmerican Journal of Agricultural Economics
Volume85
Issue number4
DOIs
StatePublished - Nov 2003

Keywords

  • Autocorrelation
  • Efficient regression models
  • Heteroskedasticity
  • Nonnormality
  • Partially adaptive estimation
  • Skewness

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