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Learning about the Long Run
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AI
DOI：10.1086/730207.png)
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摘要
En 中文
Forecasts of professional forecasters are anomalous: they are biased, and forecast errors are autocorrelated and predictable by forecast revisions. We propose that these anomalies arise because professional forecasters do not know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the world can generate all the prominent aggregate anomalies emphasized in the literature. We show this for professional forecasts of nominal interest rates and Congressional Budget Office forecasts of gross domestic product growth. Our learning model for interest rates can explain observed deviations from the expectations hypothesis of the term structure without relying on time variation in risk premia.
Keyword:
TERM STRUCTURE
EXPECTATIONS HYPOTHESIS
INTEREST-RATES
INFORMATION
RISK
INFLATION
BEHAVIOR
MODEL
PREDICTABILITY
COINTEGRATION
期刊
IF:
6.3
论文数:
2.6K
被引数:
3.2W
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