Learning and Endogenous Business Cycles in a Standard Growth Model
AbstractCyclical or chaotic competitive equilibria that do not exist under perfect foresight are shown to occur in a decentralized growth model under constant gain adaptive learning. This paper considers an economy populated by boundedly rational households making one-period ahead constant gain adaptive input price forecasts, and using simple expectation rules to predict long-run physical capital holdings and consumption. Under these hypotheses, lifetime decisions are derived as time unfolds, and analytical solutions to the representative household's problem exist for a standard class of preferences. Under various characteristics of the model's functional forms, competitive equilibrium trajectories under learning may exhibit opposite local stability properties depending whether the underlying information set accommodates all contemporary data. Calibrated to the U.S. economy, the model with boundedly rational households may exhibit endogenous business cycles around the permanent regime which is a saddle point under perfect foresight
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 240.
Date of creation: 11 Nov 2005
Date of revision:
bounded rationality; constant gain adaptive learning; endogenous business cycles;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-11-19 (All new papers)
- NEP-DGE-2005-11-19 (Dynamic General Equilibrium)
- NEP-MAC-2005-11-19 (Macroeconomics)
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