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Dynamic Asymptotically Ideal Models and Finite Approximation

Author

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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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    Citations

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    Cited by:

    1. Monia Landolsi & Kamel Bel Hadj Miled, 2024. "Semi-Nonparametric Estimation of Energy Demand in Tunisia," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 254-263, January.
    2. Drake, Leigh & Fleissig, Adrian R., 2008. "A Note On The Policy Implications Of Using Divisia Consumption And Monetary Aggregates," Macroeconomic Dynamics, Cambridge University Press, vol. 12(1), pages 132-149, February.
    3. 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).
    4. 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.
    5. Ali Jadidzadeh & Apostolos Serletis, 2019. "The Demand for Assets and Optimal Monetary Aggregation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(4), pages 929-952, June.
    6. Binner, Jane M. & Chaudhry, Sajid & Kelly, Logan & Swofford, James L., 2018. "“Risky” monetary aggregates for the UK and US," Journal of International Money and Finance, Elsevier, vol. 89(C), pages 127-138.
    7. 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).
    8. 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.
    9. Fleissig, Adrian R. & Jones, Barry E., 2023. "U.K. household-sector money demand during Brexit and the pandemic," Economic Modelling, Elsevier, vol. 123(C).
    10. 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.
    11. Sarwar, haroon & Hussian, zakir & Awan, masood sarwar, 2011. "Money Demand Functions for Pakistan (Divisia Approach)," MPRA Paper 34361, University Library of Munich, Germany.
    12. 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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