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Dynamic Asymptotically Ideal Models and Finite Approximation

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  • Fleissig, Adrian R
  • Swofford, James L

Abstract

The authors extend W. A. Barnett and A. Jonas's (1983) 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.

Suggested Citation

  • Fleissig, Adrian R & Swofford, James L, 1997. "Dynamic Asymptotically Ideal Models and Finite Approximation," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 482-492, October.
  • Handle: RePEc:bes:jnlbes:v:15:y:1997:i:4:p:482-92
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    References listed on IDEAS

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    Cited by:

    1. Brant, Molly & Marsh, Thomas L. & Featherstone, Allen M. & Crespi, John M., 2005. "Multivariate AIM Consumer Demand Model Applied to Dried Fruit, Raisins, and Dried Plums," 2005 Annual meeting, July 24-27, Providence, RI 19291, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Haroon Sarwar & Zakir Hussain & Masood Sarwar, 2011. "A Semi-Nonparametric Approach to the Demand for Money in Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 16(2), pages 87-110, Jul-Dec.
    3. Hilmer, Christiana E. & Holt, Matthew T., 2000. "A Comparison Of Resampling Techniques When Parameters Are On A Boundary: The Bootstrap, Subsample Bootstrap, And Subsample Jackknife," 2000 Annual meeting, July 30-August 2, Tampa, FL 21810, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Fleissig, Adrian R. & Kastens, Terry & Terrell, Dek, 2000. "Evaluating the semi-nonparametric fourier, aim, and neural networks cost functions," Economics Letters, Elsevier, vol. 68(3), pages 235-244, September.
    5. Drake, Leigh & Fleissig, Adrian R., 2010. "Substitution between monetary assets and consumer goods: New evidence on the monetary transmission mechanism," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2811-2821, November.
    6. Sarwar, haroon & Hussian, zakir & Awan, masood sarwar, 2011. "Money Demand Functions for Pakistan (Divisia Approach)," MPRA Paper 34361, University Library of Munich, Germany.
    7. Serletis, Apostolos & Rangel-Ruiz, Ricardo, 2005. "Microeconometrics and measurement matters: Some results from monetary economics for Canada," Journal of Macroeconomics, Elsevier, vol. 27(2), pages 307-330, June.

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