ESTIMATION OF EFFICIENT REGRESSION MODELS FOR APPLIED AGRICULTURAL ECONOMICS RESEARCH

provided by Research Papers in Economics

ESTIMATION OF EFFICIENT REGRESSION MODELS

FOR APPLIED AGRICULTURAL ECONOMICS RESEARCH

by

Octavio A. Ramfiez

Sukant K. Misra

and

Jeannie Nelson¹

1 Associate Professors and M.S. student, respectively. Department of Agricultural and Applied
Economics, Texas Tech University, Box 42132, Lubbock TX 79409-2132. Phone: (806) 742-2821,
Fax (806) 742-1099, Email (corresponding author): Octavio [email protected] edu

2002 Annual Meeting of the American Agricultural Economics Association

Long Beach, CA, July 28-31, 2002

ABSTRACT

This paper proposes and explores the use of a partially adaptive estimation technique to improve the reliability
of the inferences made from multiple regression models when the dependent variable is not normally
distributed. The relevance of this technique for agricultural economics research is evaluated through Monte
Carlo simulation and two mainstream applications: A time-series analysis of agricultural commodity prices
and an empirical model of the West Texas cotton basis. It is concluded that, given non-normality, this
technique can substantially reduce the magnitude of the standard errors of the slope parameter estimators in
relation to OLS, GLS and other least squares based estimation procedures, in practice, allowing for more
precise inferences about the existence, sign and magnitude of the effects of the independent variables on the
dependent variable of interest. In addition, the technique produces confidence intervals for the dependent
variable forecasts that are more efficient and consistent with the observed data.

Key Words : Efficient regression models, partially adaptive estimation, non-normality, skewness,
heteroskedasticity, autocorrelation.

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