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Within a New Keynesian model, we incorporate bounded rationality at the individual agent level, and we determine restrictions on expectations operators sufficient to imply aggregate IS and AS relations of the same functional form as those under rationality. This result provides dual...
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Regime-switching rational expectations models, in which the parameters of the model evolve according to a finite state Markov process, have properties that differentiate them from linear models. Issues that are well understood in linear contexts, such as equilibrium determinacy and stability...
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This paper examines the implications of forward- and backward-looking monetary policy rules in an environment with monetary-fiscal interactions. We find that the unique stationary rational expectations equilibrium (REE) is always non-Ricardian under simple implementable monetary policy rules....
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A restricted-perceptions equilibrium exists in which risk-averse agents believe stock prices follow a random walk with a conditional variance that is self-fulfilling. When agents estimate risk, bubbles and crashes arise. These effects are stronger when agents allow for ARCH in excess returns.
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This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock¡¯s return....
Persistent link: https://www.econbiz.de/10008622068
This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock's return. Recursive...
Persistent link: https://www.econbiz.de/10009150609
Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the...
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