Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-12-01T04:51:40.177Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adam, Klaus, Evans, George W., and Honkapohja, Seppo (2006) Are hyperinflation paths learnable? Journal of Economic Dynamics and Control 30, 2725–2748.CrossRefGoogle Scholar
Bäck, Thomas, Fogel, David, and Michalewicz, Zbigniew (2000) Evolutionary Computation 1. Bacis Algorithms and Operators. Bristol and Philadelphia: Institute of Physics Publishing.CrossRefGoogle Scholar
Benveniste, Albert, Metivier, Michel, and Priouret, Pierre (1990) Adaptive Algorithms and Stochastic Approximations. New York: Springer-Verlag.CrossRefGoogle Scholar
Branch, William A. and Evans, George W. (2006) A simple recursive forecasting model. Economics Letters 91, 158–166.CrossRefGoogle Scholar
Bullard, James B. (1991) Learning, rational expectations and policy: A summary of recent research. Federal Reserve Bank of St. Louis Review January–February, 50–60.CrossRefGoogle Scholar
Bullard, James B. (2006) The learnability criterion and monetary policy. Federal Reserve Bank of St. Louis Review 88 (3), 203–217.Google Scholar
Bullard, James B. and Duffy, John (2004) Learning and Structural Change in Macroeconomic Data. Federal Reserve Bank of St. Louis working paper 2004–016A.CrossRefGoogle Scholar
Bullard, James B. and Mitra, Kaushik (2002) Learning about monetary policy rules. Journal of Monetary Economics 49 (6), 1105–1129.CrossRefGoogle Scholar
Cagan, Phillip (1956) The monetary dynamics of hyperinflation. In Friedman, Milton (ed.), Studies in the Quantity Theory of Money, pp. 25–117. Chicago: The University of Chicago Press.Google Scholar
Chakraborty, Avik and Evans, George W. (2008) Can perpetual learning explain the forward premium puzzle? Journal of Monetary Economics 55, 477–490.CrossRefGoogle Scholar
Cho, In-Koo and Kasa, Kenneth (2008) Learning dynamics and endogenous currency crisis. Macroeconomic Dynamics 12 (2), 257–285.CrossRefGoogle Scholar
Cho, In-Koo and Sargent, Thomas J. (1997) Learning to Be Credible. Working paper. Available at http://homepages.nyu.edu/~ts43/RESEARCH/research.htm#Learning.Google Scholar
Evans, George W. and Honkapohja, Seppo (1993) Adaptive forecasts, hysteresis, and endogenous fluctuations. Federal Reserve Bank of San Francisco Economic Review 1, 3–13.Google Scholar
Evans, George W. and Honkapohja, Seppo (2001) Expectations and Learning in Macroeconomics. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Evans, George W. and Ramey, Garey (2006) Adaptive expectations, underparameterization and the Lucas critique. Journal of Monetary Economics 53, 249–264.CrossRefGoogle Scholar
Funke, Michael, Hall, Stephen, and Sola, Martin (1994) Rational bubbles during Polands' hyperinflation: Implications and empirical evidence. European Economic Review 38 (6), 1257–76.CrossRefGoogle Scholar
Goldberg, David E. (1989) Genetic Algorithms in Search, Optimizations, and Machine Learning. Reading, MA: Addison–Wesley.Google Scholar
Gourieroux, Christian, Monfort, Alain, and Renault, Eric (1993) Indirect inference. Journal of Applied Econometrics. Issue Supplement: Special Issue on Econometric Inference Using Simulation Techniques 8, 85–118.Google Scholar
Holland, John H. (1975) Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.Google Scholar
Khan, Mohsin S. (1977) The variability of expectations in hyperinflations. Journal of Political Economy 85 (4), 817–827.CrossRefGoogle Scholar
Kushner, Harold J. and Yang, Jichuan (1995) Analysis of adaptive step-size SA algorithms for parameter tracking. IEEE Transactions on Automatic Control 40 (8), 1403–1410.CrossRefGoogle Scholar
Kushner, Harold J. and Yin, G. George (2003) Stochastic Approximation and Recursive Algorithms and Applications. 2nd ed.New York: Springer-Verlag.Google Scholar
Kydland, Finn E. and Prescott, Edward C. (1977) Rules rather than discretion: The inconsistency of optimal plans. Journal of Political Economy 85, 473–491.CrossRefGoogle Scholar
Ljung, Lennart and Söderström, Torsten (1983) Theory and Practice of Recursive Identification. Cambridge, MA: MIT Press.Google Scholar
Marcet, Albert and Nicolini, Juan Pablo (2003) Recurrent hyperinflations and learning. American Economic Review 93 (5), 1476–1498.CrossRefGoogle Scholar
Marcet, Albert and Sargent, Thomas J. (1989a) Convergence of least squares learning mechanisms in self-referential linear stochastic models. Journal of Economic Theory 48, 337–368.CrossRefGoogle Scholar
Marcet, Albert and Sargent, Thomas J. (1989b) Least squares learning and the dynamics of hyperinflations. In Barnett, William A., Geweke, John, and Shell, Karl (eds.), Economic Complexity: Chaos, Sunspots, bubbles, and Nonlinearity: Proceedings of the Fourth International Symposium in Economic Theory and Econometrics, pp. 119–137. Cambridge, UK: Cambridge University Press.Google Scholar
Michalewicz, Zbigniew (1996) Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed.Berlin: Springer-Verlag.CrossRefGoogle Scholar
Milani, Fabio (2007) Expectations, learning and macroeconomic persistence. Journal of Monetary Economics 54 (7), 2065–2082.CrossRefGoogle Scholar
Milani, Fabio (2008) Learning, monetary policy rules, and macroeconomic stability. Journal of Economic Dynamics and Control 32 (10), 3148–3165.CrossRefGoogle Scholar
Mussa, Michael (1981) Sticky prices and disequilibrium adjustment in a rational model of the inflationary process. American Economic Review 71 (5), 1020–1027.Google Scholar
Orphanides, Athanasios and Williams, John C. (2004) The Decline of Activist Stabilization Policy. Board of Governors of the Federal Reserve System working paper 804.Google Scholar
Orphanides, Athanasios and Williams, John C. (2005) Imperfect knowledge, inflation expectations, and monetary policy. In Bernanke, Ben S. and Woodford, Michael (eds.), The Inflation-Targeting Debate, pp. 201–234. Chicago: NBER and University of Chicago Press.Google Scholar
Sargent, Thomas J. (1999) The Conquest of American Inflation. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Sargent, Thomas J. and Wallace, Neil (1987) Inflation and government budget constraint. In Razin, Assaf and Sadka, Efraim (eds.), Economic Policy in Theory and Practice, pp. 170–200. New York: St.Martin's Press.CrossRefGoogle Scholar
Sargent, Thomas J., Williams, Noah, and Zha, Tao A. (2006) The Conquest of South American Inflation. NBER working paper series 12606.CrossRefGoogle Scholar
Schwefel, Hans-Paul (2000) Advantages (and disadvantages) of evolutionary computation over other approaches. In Bäck, Thomas, Fogel, David, and Michalewicz, Zbigniew (eds.), Evolutionary Computation 1. Bacis Algorithms and Operators. Bristol and Philadelphia: Institute of Physics Publishing.Google Scholar
Silveira, Antonio M. (1973) The demand for money: The evidence from the Brazilian economy. Journal of Money, Credit and Banking 5 (1), 113–140.CrossRefGoogle Scholar
Timmermann, Allan G. (1993) How learning in financial markets generates excess volatility and predictability is stock prices. Quarterly Journal of Economics 108 (4), 1135–1145.CrossRefGoogle Scholar