论文标题
简单模型和偏见的预测
Simple Models and Biased Forecasts
论文作者
论文摘要
本文提出了一个框架，其中限制了代理商使用简单模型来预测经济变量并表征所得偏见。它认为只能使用不超过D状态的状态空间模型的代理，其中D可以衡量模型的跨颞复杂性。代理人的理性是，他们只能考虑太简单而无法捕获真实过程的模型，但是他们在被考虑的模型中使用了最佳模型。使用简单的模型增加了前瞻性决策的持久性，并增加了它们之间的共同点。这种机制缩小了商业周期理论与数据之间的差距。在新的新古典综合模型中，使用简单模型的假设比理性期望假设要拟合数据要拟合得更好。此外，简单的模型同时解决了Barro-King和远期指导难题，同时改善了TFP冲击的传播。
This paper proposes a framework in which agents are constrained to use simple models to forecast economic variables and characterizes the resulting biases. It considers agents who can only entertain state-space models with no more than d states, where d measures the intertemporal complexity of a model. Agents are boundedly rational in that they can only consider models that are too simple to capture the true process, yet they use the best model among those considered. Using simple models adds persistence to forward-looking decisions and increases the comovement among them. This mechanism narrows the gap between business-cycle theory and data. In a new neoclassical synthesis model, the assumption that agents use simple models fits the data much better than the rational-expectations hypothesis. Moreover, simple models simultaneously resolve the Barro-King and forward guidance puzzles while improving the propagation of TFP shocks.
