Working Paper: CEPR ID: DP16377
Authors: Isaac Baley; Laura Veldkamp
Abstract: 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.
Keywords: Bayes Law; Passive Learning; Active Learning; Signal Extraction; Information Choice; Sticky Information; Rational Inattention; Experimentation; Data Economy; Coordination Games
JEL Codes: D80; D81; D83; D84; E20; E30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Agents' beliefs formed through Bayesian updating (D83) | Economic decisions (production and investment) (E20) |
| Precision of signals (C58) | Agents' actions (L85) |
| Agents' inability to distinguish between permanent and transitory shocks (D89) | Learning dynamics that shape expectations and actions (D84) |
| Feedback loop between economic activity and data generated by that activity (E32) | Future economic outcomes (E66) |
| Active learning strategies (C90) | Different economic behaviors compared to passive learning scenarios (E70) |