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APPLICATION OF AN ADAPTIVE STEP-SIZE ALGORITHM IN MODELS OF HYPERINFLATION

Published online by Cambridge University Press:  12 April 2012

Olena Kostyshyna
Olena Kostyshyna*
Affiliation:
Portland State University
*
Address correspondence to: Olena Kostyshyna, Department of Economics, Portland State University, 1721 SW Broadway, Portland, OR 97201, USA; e-mail: okostysh@pdx.edu.
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Abstract

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An adaptive step-size algorithm [Kushner and Yin, Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., New York: Springer-Verlag (2003)] is used to model time-varying learning, and its performance is illustrated in the environment of Marcet and Nicolini [American Economic Review 93 (2003), 1476–1498]. The resulting model gives qualitatively similar results to those of Marcet and Nicolini, and performs quantitatively somewhat better, based on the criterion of mean squared error. The model generates increasing gain during hyperinflations and decreasing gain after hyperinflations end, which matches findings in the data. An agent using this model behaves cautiously when faced with sudden changes in policy, and is able to recognize a regime change after acquiring sufficient information.

Keywords

Time-Varying GainAdaptive ExpectationsHyperinflationLearning in Macroeconomics

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 16 , Supplement S3 , November 2012 , pp. 355 - 375
Copyright
Copyright © Cambridge University Press 2012

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