Improving monetary policy models

Christopher A. Sims

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


If macroeconomic models are to be useful in policy-making, where uncertainty is pervasive, the models must be treated as probability models, whether formally or informally. Use of explicit probability models allows us to learn systematically from past mistakes, to integrate model-based uncertainty with uncertain subjective judgment, and to bind data-based forecasting together with theory-based projection of policy effects. Yet in the last few decades policy models at central banks have steadily shed any claims to being believable probability models of the data to which they are fit. Here we describe the current state of policy modeling, suggest some reasons why we have reached this state, and assess some promising directions for future progress.

Original languageEnglish (US)
Pages (from-to)2460-2475
Number of pages16
JournalJournal of Economic Dynamics and Control
Issue number8
StatePublished - Aug 2008

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Control and Optimization
  • Applied Mathematics


  • Bayesian inference
  • Central bank models
  • Econometric models


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