Learning and the Yield Curve
研究发现理性预期假说与收益率曲线数据存在两个矛盾，提出有限理性代理人通过适应性学习形成预期的优化框架，能解释数据中的收益率模式并部分解决波动性谜题。
Two central implications of Expectations Hypothesis under rational expectations are inconsistent with yield curve data: (i) future expected long yields fall, instead of rising, when yield spread rises; (ii) long yields are excessively volatile relative to short yields. I propose an optimization framework in which boundedly rational agents use adaptive learning to form expectations. The belief structure rationalizes pattern of yields observed in the data so that the first puzzle does not arise with subjective expectations: intertemporal income and substitution effects are amplified relative to rational expectations. The second puzzle is partly accounted for by extra volatility due to parameter uncertainty.