Dynamic Asymptotically Ideal Models and Finite Approximation

Adrian R. Fleissig · James L. Swofford

32 citations0/0 files storedJournal of Business and Economic Statistics

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Forecasting Techniques and Applications Statistical Methods and Applications Statistical and numerical algorithms

Abstract

We extend Barnett and Jonas's asymptotically ideal model (AIM) to model for the possibility that the data were generated by a dynamic process. Prediction errors for dynamic and static AIM models are compared for various simulated datasets. Monetary data are also used to evaluate the AIM specifications. There is substantial evidence that an AR(1) correction considerably improves the quality of low-order finite approximations of AIM with the cost of estimating only one additional parameter. Furthermore, restricting a dynamic AIM to approximate only linear homogenous functions often results in severe misspecification.

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References

2019American Economic Review 1973Transcendental Logarithmic Production Frontiers 1981On the bias in flexible functional forms and an essentially unbiased form 1970Habit Formation and Dynamic Demand Functions 1962Production Functions with Constant Elasticities of Substitution 1978Superlative Index Numbers and Consistency in Aggregation 1980Global Properties of Flexible Functional Forms 1983A Comparison of the Performance of Three Flexible Functional Forms

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