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Gresham’S Law Of Model Averaging

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Abstract

A decision maker doubts the stationarity of his environment. In response, he uses two models, one with time-varying parameters, and another with constant parameters. Forecasts are then based on a Bayesian Model Averaging strategy, which mixes forecasts from the two models. In reality, structural parameters are constant, but the (unknown) true model features expectational feedback, which the reduced form models neglect. This feedback permits fears of parameter instability to become self-confirming. Within the context of a standard linear present value asset pricing model, we use the tools of large deviations theory to show that even though the constant parameter model would converge to the (constant parameter) Rational Expectations Equilibrium if considered in isolation, the mere presence of an unstable alternative drives it out of consideration.

Suggested Citation

  • In-Koo Cho & Kenneth Kasa, 2016. "Gresham’S Law Of Model Averaging," Discussion Papers dp16-06, Department of Economics, Simon Fraser University.
  • Handle: RePEc:sfu:sfudps:dp16-06
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    Cited by:

    1. Gelfer, Sacha, 2020. "The effects of professional forecast dissemination on macroeconomic volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 131-156.
    2. Tortorice, Daniel L, 2018. "The business cycle implications of fluctuating long run expectations," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 266-291.

    More about this item

    Keywords

    model averaging; asset pricing;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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