A Unified Model of Learning to Forecast
提出了一个有限理性异质性预期的统一模型，整合了适应性学习、k级推理和复制动态，解释了学习预测实验中市场收敛的条件，并在新凯恩斯模型中应用于前瞻指导政策研究。
We propose a model of boundedly rational and heterogeneous expectations that unifies adaptive learning, k-level reasoning, and replicator dynamics. Level-0 forecasts evolve over time via adaptive learning. Agents revise over time their depth of reasoning in response to forecast errors, observed and counterfactual. The unified model makes sharp predictions for when and how quickly markets converge in Learning-to-Forecast Experiments, including novel predictions for individual and market behavior in response to announced events. We present experimental results that support these predictions. We apply our unified approach in the New Keynesian model to study forward guidance policy.