Asymptotic Likelihood-Based Prediction Functions

Thomas Cooley and William R Parke

Econometrica, 1990, vol. 58, issue 5, 1215-34

Abstract: This paper develops asymptotic prediction functions that approximate the shape of the density of future observations and correct for parameter uncertainty. The functions are based on extensions to a definition of predictive likelihood originally suggested by S. L. Lauritzen (1974) and D. Hinkley (1979). The prediction function is shown to possess efficiency properties based on the Kullback-Leibler measure of information loss. Examples of the application of the prediction function and the derivation of relative efficiency are shown for linear-normal models, nonnormal models, and ARCH models. Copyright 1990 by The Econometric Society.

Date: 1990
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Related works:
Working Paper: ASYMPTOTIC LIKELIHOOD BASED PREDICTION FUNCTIONS (1988)
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