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Efficient Estimation of Agricultural Time Series Models with Nonnormal Dependent Variables
Author(s) -
Ramírez Octavio A.,
Misra Sukant K.,
Nelson Jeannie
Publication year - 2003
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/1467-8276.00505
Subject(s) - heteroscedasticity , autocorrelation , statistics , econometrics , monte carlo method , series (stratigraphy) , variance (accounting) , mathematics , least squares function approximation , estimation , economics , estimator , paleontology , accounting , biology , management
This article proposes using an expanded form of the Johnson S U 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.