Shepherd, Ben (2011): When are adaptive expectations rational? A generalization.
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Abstract
This note presents a simple generalization of the adaptive expectations mechanism in which the learning parameter is time variant. It is shown that expectations generated in this way are rational in the sense of producing minimum mean squared forecast errors for a broad class of time series models, namely any process that can be written in linear state space form.
Item Type: | MPRA Paper |
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Original Title: | When are adaptive expectations rational? A generalization |
Language: | English |
Keywords: | Adaptive Expectations; Rational Expectations; Kalman Filter |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 34644 |
Depositing User: | Ben Shepherd |
Date Deposited: | 11 Nov 2011 15:38 |
Last Modified: | 26 Sep 2019 16:15 |
References: | Cuthbertson, K. 1988. “Expectations, Learning, and the Kalman Filter.” The Manchester School, 56(3): 223-246. Durbin, J., and S.J. Koopman. 2001. Time Series Analysis by State Space Methods. Oxford: Oxford University Press. Farmer, R.E.A. 2002. “Why Does Data Reject the Lucas Critique?” Annales d’Economie et de Statistique, 67/68: 111-129. Harvey, A.C. 1989. Forecasting, Structural Time Series Models, and the Kalman Filter. Cambridge: Cambridge University Press. Muth, J.F. 1960. “Optimal Properties of Exponentially Weighted Forecasts.” Journal of the American Statistical Association, 55(290): 299-305. Nerlove, M. 1956. “Estimates of the Elasticity of Supply of Selected Agricultural Commodities.” Journal of Farm Economics, 38: 492-509. Nerlove, M., and S. Wage. 1964. “On the Optimality of Adaptive Forecasting.” Management Science, 10(2): 207-224. Theil, H., and S. Wage. 1964. “Some Observations on Adaptive Forecasting.” Management Science, 10(2): 198-206. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/34644 |