Learning about Consumption Dynamics
从贝叶斯学习视角刻画美国消费动态，发现与完全信息理性预期基准相比，学习产生的信念能显著改善资产定价模型对典型事实和市场价格股息比的拟合。
ABSTRACT This paper characterizes U.S. consumption dynamics from the perspective of a Bayesian agent who does not know the underlying model structure but learns over time from macroeconomic data. Realistic, high‐dimensional macroeconomic learning problems, which entail parameter, model, and state learning, generate substantially different subjective beliefs about consumption dynamics compared to the standard, full‐information rational expectations benchmark. Beliefs about long‐run dynamics are volatile, with counter‐cyclical conditional volatility, and drift over time. Embedding these beliefs in a standard asset pricing model significantly improves the model's ability to match the stylized facts, as well as the sample path of the market price‐dividend ratio.