全球智库信息集成服务系统(G2TT): Bayesian Learning

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w29338
来源ID	Working Paper 29338
	Bayesian Learning
	Isaac Baley; Laura Veldkamp
发表日期	2021-10-04
出版年	2021
语种	英语
摘要	We survey work using Bayesian learning in macroeconomics, highlighting common themes and new directions. First, we present many of the common types of learning problems agents face---signal extraction problems---and trace out their effects on macro aggregates, in different strategic settings. Then we review different perspectives on how agents get their information. Models differ in their motives for information acquisition and the cost of information, or learning technology. Finally, we survey the growing literature on the data economy, where economic activity generates data and the information in data feeds back to affect economic activity.
主题	Macroeconomics ; Financial Economics ; Financial Markets
URL	https://www.nber.org/papers/w29338
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/587012
推荐引用方式 GB/T 7714	Isaac Baley,Laura Veldkamp. Bayesian Learning. 2021.