On learnability of Eâ€“stable equilibria
AbstractWhile under recursive least squares learning the dynamics of the economy converges to rational expectations equilibria (REE) which are Eâ€“stable, some recent examples propose that Eâ€“stability is not a sufficient condition for learnability. In this paper, we provide some further evidence on the conditions under which Eâ€“stability of a particular equilibrium might fail to imply its stochastic gradient (SG) or generalized SG learnability. We also claim that the requirement on the speed of convergence of the learning process imposed by  also implies that Eâ€“stable equilibria are likely to be GSG learnable. We show this in a simple â€New Keneysianâ€ model of optimal monetary policy design in which the stability of REE under SG learning. In this case, the paper gives the conditions which are necessary for reversal of learnability
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 451.
Date of creation: 04 Jul 2006
Date of revision:
Adaptive learning; Eâ€“stability; stochastic gradient; learnability;
Find related papers by JEL classification:
- C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-EVO-2006-07-15 (Evolutionary Economics)
- NEP-MAC-2006-07-15 (Macroeconomics)
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