Code for: Optimally Imprecise Memory and Biased Forecasts
收藏ICPSR2024-01-01 更新2026-04-16 收录
下载链接：
https://www.openicpsr.org/openicpsr/project/206101/version/V1/view
下载链接
链接失效反馈官方服务：
资源简介：
We propose a model of optimal decision making subject to a memory constraint. The constraint is a limit on the precision of memory measured using Shannon's mutual information, as in models of rational inattention; but our theory differs from that of Sims (2003) in not assuming costless memory of past cognitive states. We show that the model implies that both forecasts and actions will exhibit idiosyncratic random variation; that average beliefs will also differ from rational-expectations beliefs, with a bias that fluctuates forever with a variance that does not fall to zero even in the long run; and that more recent news will be given disproportionate weight in forecasts. We solve the model under a variety of assumptions about the degree of persistence of the variable to be forecasted and the horizon over which it must be forecasted, and examine how the nature of forecast biases depends on these parameters. The model provides a simple explanation for the over-reaction to news observed in the laboratory by Afrouzi et al. (2023).
提供机构：
Columbia University; Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Institute of Molecular and Clinical Ophthalmology Basel, and University of Basel; Federal Reserve Bank of San Francisco
创建时间：
2024-01-01
