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Learning and Macroeconomics
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DOI：10.1146/annurev.economics.050708.142927.png)
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Abstract
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Expectations play a central role in modern macroeconomic theories. The econometric learning approach models economic agents as forming expectations by estimating and updating forecasting models in real time. The learning approach provides a stability test for rational expectations and a selection criterion in models with multiple equilibria. In addition, learning provides new dynamics if older data are discounted, if models are misspecified, or if agents choose between competing models. This paper describes the expectational stability (E-stability) principle and the stochastic approximation tools used to assess equilibria under learning. Applications of learning to a number of areas are reviewed, including the design of monetary and fiscal policy, business cycles, self-fulfilling prophecies, hyperinflation, liquidity traps, and asset prices.
Keywords:
E-stability
stochastic approximation
persistent learning dynamics
business cycles
monetary policy
asset prices
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