Abstract
Economic agents who are uncertain of their economic model learn, and this learning is sensitive to the presence of data uncertainty. I investigate this idea in a framework that successfully describes inflation as a learning Federal Reserve's optimal policy but fails to satisfactorily motivate these policy shifts. I modify the framework to account for data uncertainty: the learning process is made more sluggish by its presence. Consequently, the estimated model provides an explanation for the rise and fall in inflation: the concurrent rise and fall in the perceived Philips curve trade-off between inflation and unemployment.
Original language | English (US) |
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Pages (from-to) | 341-365 |
Number of pages | 25 |
Journal | Journal of Money, Credit and Banking |
Volume | 44 |
Issue number | 2-4 |
DOIs | |
State | Published - Mar 2012 |
Externally published | Yes |
Keywords
- Data uncertainty
- Extended Kalman filter
- Learning
- Markov-chain Monte Carlo
- Model uncertainty
- Monetary policy
- Optimal control
- Parameter uncertainty
- Real-time data
ASJC Scopus subject areas
- Accounting
- Finance
- Economics and Econometrics