Generalised Fading Memory Learning in a Cobweb Model: some evidence
We develop a learning rule that generalises the well known fading memory learning in the sense that the weights attached to the available time series data are not constant and are updated in light of the prediction error(s). The underlying idea is that confidence in the available data will be low when large errors have been realized (e.g. in times of higher volatility) and vice versa. A class of functional forms compatible with this idea is analysed in the context of a standard Cobweb model with boundedly rational agents. We study the problem of convergence to the perfect foresight equilibrium (both local and global) and give conditions that ensure the coexistence of different attractors. We refer to both experimental and numerical evidence to establish the possible range of application of the generalised fading memory learning
|Date of creation:||11 Aug 2004|
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- Hommes, Cars H., 1998. "On the consistency of backward-looking expectations: The case of the cobweb," Journal of Economic Behavior & Organization, Elsevier, vol. 33(3-4), pages 333-362, January.
- Hommes, Cars & Sorger, Gerhard, 1998. "Consistent Expectations Equilibria," Macroeconomic Dynamics, Cambridge University Press, vol. 2(03), pages 287-321, September.
- Potzelberger, Klaus & Sogner, Leopold, 2003. "Stochastic equilibrium: learning by exponential smoothing," Journal of Economic Dynamics and Control, Elsevier, vol. 27(10), pages 1743-1770, August.
- Domenico Colucci & V. Valori, 2001.
"Error learning behaviour and stability revisited,"
CeNDEF Workshop Papers, January 2001
1A.1, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Peiyuan Zhu & Carl Chiarella & Tony He, 2003.
"Fading Memory Learning in the Cobweb Model with Risk Averse Heterogeneous Producers,"
Computing in Economics and Finance 2003
31, Society for Computational Economics.
- Carl Chiarella & Xue-Zhong He & Peiyuan Zhu, 2003. "Fading Memory Learning in the Cobweb Model with Risk Averse Heterogeneous Producers," Research Paper Series 108, Quantitative Finance Research Centre, University of Technology, Sydney.
- Kirman, A., 1997.
"Interaction and Markets,"
ASSET - Instituto De Economia Publica
166, ASSET (Association of Southern European Economic Theorists).
- Williams, Arlington W, 1987. "The Formation of Price Forecasts in Experimental Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 19(1), pages 1-18, February.
- Lovell, Michael C, 1986. "Tests of the Rational Expectations Hypothesis," American Economic Review, American Economic Association, vol. 76(1), pages 110-24, March.
- Lucas, Robert E, Jr, 1986. "Adaptive Behavior and Economic Theory," The Journal of Business, University of Chicago Press, vol. 59(4), pages S401-26, October.
- Hommes, Cars H., 1994. "Dynamics of the cobweb model with adaptive expectations and nonlinear supply and demand," Journal of Economic Behavior & Organization, Elsevier, vol. 24(3), pages 315-335, August.
- repec:cup:macdyn:v:2:y:1998:i:3:p:287-321 is not listed on IDEAS
- Hugh Kelley & Daniel Friedman, 2002. "Learning to Forecast Price," Economic Inquiry, Western Economic Association International, vol. 40(4), pages 556-573, October.
- Hey, John D., 1994. "Expectations formation: Rational or adaptive or ...?," Journal of Economic Behavior & Organization, Elsevier, vol. 25(3), pages 329-349, December.
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