Econ Papers Daily / 期刊论文
卡尔曼滤波学习、有限理性与信息一致性
Kalman Filter Learning, Bounded Rationality and Informational Consistency
摘要
We develop a behavioural New Keynesian model with rational (RE) and boundedly rational (BR) agents. The latter are anticipated utility learners and use simple heuristic rules to forecast aggregate variables exogenous to their micro‐environment. For the RE agents, we study two information assumptions: standard perfect information and imperfect information (II‐involving Kalman‐filter learning). We show that a consistent set of II for the modeller and the private sector generates endogenous persistence of decisions in response to unobserved exogenous shocks and thus RE under II dominates the BR specifications in empirical comparisons. We reveal substantial evidence of composite RE(II)‐BR structure with estimated proportions of RE and BR agents. Our results highlight the importance of informational consistency for the analysis of behavioural heterogeneity.
中文摘要
我们构建了一个包含理性（RE）与有限理性（BR）主体的行为新凯恩斯模型。后者为预期效用学习者，运用简单启发式规则预测其微观环境外生的总量变量。针对理性主体，我们研究两种信息假设：标准的完全信息与不完全信息（II——涉及卡尔曼滤波学习）。我们表明，模型设定者与私人部门之间一组一致的不完全信息假设会生成决策对未观测外生冲击响应的内生持续性，因此在实证比较中，不完全信息下的理性设定优于有限理性设定。我们揭示了大量证据支持理性（不完全信息）—有限理性复合结构的存在，并估计了理性与有限理性主体的比例。我们的结果凸显了信息一致性对于分析行为异质性的重要性。