The Mean and Variance of the Mean-Variance Decision Rule

James A. Chalfant, Robert Collender and Shankar Subramanian

American Journal of Agricultural Economics, 1990, vol. 72, issue 4, 966-974

Abstract: The widely used mean-variance approach to decisions under uncertainty requires estimates of the parameters of the joint distribution of returns. When optimal behavior is determined using estimates, rather than the true values, the decision is a random variable. We consider the reliability of mean-variance analysis by examining the bias and variance-covariance matrix for the decision vector. The latter shows that decisions based on estimated parameters can have a large variance around the true optimum. The results show that optimal decisions can differ substantially from those based on mean-variance analysis.

Date: 1990
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:72:y:1990:i:4:p:966-974.

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