Uncertainty Over Models and Data: The Rise and Fall of American Inflation

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)341-365
Number of pages25
JournalJournal of Money, Credit and Banking
Volume44
Issue number2-4
DOIs
StatePublished - Mar 2012
Externally publishedYes

Fingerprint

Inflation
Uncertainty
Economics
Trade-offs
Learning process
Federal Reserve
Unemployment
Optimal policy

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

  • Finance
  • Accounting
  • Economics and Econometrics

Cite this

Uncertainty Over Models and Data : The Rise and Fall of American Inflation. / Pruitt, Seth.

In: Journal of Money, Credit and Banking, Vol. 44, No. 2-4, 03.2012, p. 341-365.

Research output: Contribution to journalArticle

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