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Monetary Policy with Model Uncertainty: Distribution Forecast Targeting

  • Noah Williams
  • Lars E.O. Svensson

We examine optimal and other monetary policies in a linear-quadratic setup with relatively general forms of model uncertainty. The forms of uncertainty our framework encompasses include: simple i.i.d. model deviations; serially correlated model deviations; estimable regime-switching models; more complex structural uncertainty, such as uncertainty about a set of structurally very different models, for instance, backward- and forward-looking models; central-bank judgment about the state of model uncertainty; and so forth. We provide an algorithm for finding the optimal policy as well as solutions for arbitrary reaction functions. This allows us to compute and plot consistent distribution forecasts---fan charts---of target variables and instruments. Our methods hence extend certainty equivalence and "mean forecast targeting" to the more general certainty non-equivalence and "distribution forecast targeting."

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 108.

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Date of creation: 11 Nov 2005
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Handle: RePEc:sce:scecf5:108
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  1. Troy Davig & Eric M. Leeper, 2006. "Generalizing the Taylor Principle," Caepr Working Papers 2006-001, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  2. Linde, Jesper, 2005. "Estimating New-Keynesian Phillips curves: A full information maximum likelihood approach," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1135-1149, September.
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