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Monetary Policy in an Uncertain World: Probability Models and the Design of Robust Monetary Rules

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  • Paul Levine

    (University of Surrey)

Abstract

The past forty years or so has seen a remarkable transformation in macro-models used by central banks, policymakers and forecasting bodies. This papers describes this transformation from reduced-form behavioural equations estimated separately, through to contemporarymicro-founded dynamic stochastic general equilibrium (DSGE) models estimated by systems methods. In particular by treating DSGE models estimated by Bayesian-Maximum-Likelihood methods I argue that they can be considered as probability models in the sense described by Sims (2007) and be used for risk-assessment and policy design. This is true for any one model, but with a range of models on offer it is possible also to design interest rate rules that are simple and robust across the rival models and across the distribution of parameter estimates for each of these rivals as in Levine et al. (2008). After making models better in a number of important dimensions, a possible road ahead is to consider rival models as being distinguished by the model of expectations. This would avoid becoming 'a prisoner of a single system' at least with respect to expectations formation where, as I argue, there is relatively less consensus on the appropriate modelling strategy.

Suggested Citation

  • Paul Levine, 2010. "Monetary Policy in an Uncertain World: Probability Models and the Design of Robust Monetary Rules," School of Economics Discussion Papers 0210, School of Economics, University of Surrey.
  • Handle: RePEc:sur:surrec:0210
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    Cited by:

    1. Simplice Asongu, 2016. "New empirics of monetary policy dynamics: evidence from the CFA franc zones," African Journal of Economic and Management Studies, Emerald Group Publishing, vol. 7(2), pages 164-204, June.
    2. Simplice A. Asongu, 2014. "Does money matter in Africa?: New empirics on long- and short-run effects of monetary policy on output and prices," Indian Growth and Development Review, Emerald Group Publishing, vol. 7(2), pages 142-180, November.
    3. Ghate, Chetan & Pandey, Radhika & Patnaik, Ila, 2013. "Has India emerged? Business cycle stylized facts from a transitioning economy," Structural Change and Economic Dynamics, Elsevier, vol. 24(C), pages 157-172.
    4. Holtemöller, Oliver & Mallick, Sushanta, 2015. "Global Food Prices and Business Cycle Dynamics in an Emerging Market Economy," IWH Discussion Papers 15/2015, Halle Institute for Economic Research (IWH).
    5. Holtemöller, Oliver & Mallick, Sushanta, 2016. "Global food prices and monetary policy in an emerging market economy: The case of India," Journal of Asian Economics, Elsevier, vol. 46(C), pages 56-70.

    More about this item

    Keywords

    structured uncertainty; DSGE models; robustness; Bayesian estimation; interest-rate rules;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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