An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy

Fax: +41 61 / 280 91 00 and +41 61 / 280 81 00This publication is available on the BIS website (www.bis.org). This article analyses the welfare consequences of delegating to the central bank the task of minimising deviations of forecasts of goal variables from their target values. The delegated objectives considered in this article are motivated by the observation that central banks oftentimes operate under objectives which do not necessarily represent society's preferences. The analysis is performed using an estimated model of optimising households and firms that generates tradeoffs between stabilising wage and price inflation and the output gap. We find that when the central bank's objective is defined solely in terms of price inflation, it is welfare optimal to stabilise only those fluctuations in price inflation that are forecastable at least five quarters ahead. On the other hand, when the central bank's objective involves both wage and price inflation stabilisation, the central bank should stabilise all fluctuations in these variables, not just those forecastable at some horizon. © Bank for

Section: An Estimated Model For Policy Evaluationcontrasting

confidence: 99%

Section: An Estimated Model For Policy Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecast-based Monetary Policy

Amato

Laubach

2000

“…In this final step, we are essentially solving a linear-quadratic (LQ) problem with rational expectations. The LQ approach has become a popular tool in studying optimal monetary policy in closed economy models with a single sector (e.g., Rotemberg and Woodford (1997)) or multiple sectors (e.g., Erceg, et al (2000), Huang and Liu (2004b)), and in open economy models with a single traded sector (e.g., Clarida, et al (2002), Benigno and Benigno (2003), Gali andMonacelli (2002), andPappa (2004) sectors and multiple sources of nominal rigidity, for both a regime with independent central banks (i.e., the Nash regime) and one with cooperating central banks (i.e., the cooperating regime). 8…”

Section: Optimal Monetary Policymentioning

confidence: 99%

Gains from Coordination in a Multi-Sector Open Economy: Does it Pay to be Different?

Liu

Pappa

2005

“…Given the complexities involved in estimating stochastic general equilibrium models and the difficulties in designing criteria which are informative about their discrepancy with the data, a portion of the literature has also considered less demanding limited information methods and focused on whether a model matches the data only along certain dimensions. For example, following Rotemberg and Woodford (1997), Christiano, et. al.…”

Section: Introductionmentioning

confidence: 99%

How Much Structure in Empirical Models?

Canova

2007

This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data. JEL classification numbers: C10, C52, E32, E50