Decisionmetrics: A Decision-Based Approach to Econometric Modeling

Spyros Skouras

01-11-064

In many applications it is necessary to use a simple and therefore highly misspecified econometric model as the basis for decision making. We propose an approach to developing a possibly misspecified econometric model that will be used as the beliefs of an objective expected utility maximizer. A discrepancy between model and “truth” is introduced that is interpretable as a measure of the model’s value for this decisionmaker. Our decision-based approach utilizes this discrepancy in estimation, selection, inference, and evaluation of parametric or semiparametric models. The methods proposed nest quasi-likelihood methods as a special case that arises when model value is measured by the Kullback-Leibler information discrepancy and also provide an econometric approach for developing parametric decision rules (e.g., technical trading rules) with desirable properties. The approach is illustrated and applied in the context of a CARA investor’s decision problem for which analytical, simulation, and empirical results suggest it is very effective.