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Mitigating the Effects of Multicollinearity Using Exact and Stochastic Restrictions: The Case of an Aggregate Agricultural Production Function in Thailand
Author(s) -
Mittelhammer Ron C.,
Young Douglas L.,
Tasanasanta Damrongsak,
Donnelly John T.
Publication year - 1980
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 - 10.2307/1239685
Subject(s) - multicollinearity , ordinary least squares , econometrics , production (economics) , agriculture , aggregate (composite) , production function , principal component analysis , agricultural productivity , principal component regression , economics , statistics , regression , function (biology) , aggregate data , principal (computer security) , estimation , mathematics , regression analysis , computer science , microeconomics , geography , materials science , management , archaeology , evolutionary biology , composite material , biology , operating system
Ordinary least squares, exactly restricted OLS, stochastically restricted OLS (mixed estimation), and principal components regression each were used to estimate an aggregate agricultural production function for Thailand for which data were highly multicollinear. Pretest considerations, incorporating alternative risk measures, were addressed in detail for purposes of model evaluation. The final mixed and principal components models generally outperformed OLS in terms of risk and overall reasonableness, mitigating a serious multicollinearity problem and permitting a direct examination of the rate and composition of Thai agricultural output growth.