Improving Monetary Policy Models

Christopher Sims ()

No 74, Working Papers from Princeton University, Department of Economics, Center for Economic Policy Studies.

Abstract: 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-bassed 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.

Keywords: monetary; policy (search for similar items in EconPapers)
JEL-codes: C53 E17 E52 E58 (search for similar items in EconPapers)
Date: 2006-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Improving monetary policy models (2008)
Journal Article: Improving monetary policy models (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:cepsud:128

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