The Taylor rule and forecast intervals for exchange rates
By: Jian Wang and Jason J. Wu.
Contributors: Wu, Jason J.
Series: International finance discussion papers -- no. 963; International finance discussion papers (Online) -- no. 963.
Publisher: Federal Reserve Board
Published: 2009
Place of publication: Washington, D.C
Language: eng
Format: Electronic resource
Description: "This paper attacks the Meese-Rogoff puzzle from a different perspective: out-of-sample interval forecasting.  Most studies in the literature focus on point forecasts.  In this paper, we apply Robust Semiparametric (RS) interval forecasting to a group of Taylor rule models.  Forecast intervals for twelve OECD exchange rates are generated and modified tests of Giacomini and White (2006) are conducted to compare the performance of Taylor rule models and the random walk.  Our contribution is twofold.  First, we find that in general, Taylor rule models generate tighter forecast intervals than the random walk, given that their intervals cover out-of-sample exchange rate realizations equally well.  This result is more pronounced at longer horizons.  Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting the distributions of exchange rates.  The benchmark Taylor rule model is also found to perform better than the monetary and PPP models.  Second, the inference framework proposed in this paper for forecast-interval evaluation can be applied in a broader context, such as inflation forecasting, not just to the models and interval forecasting methods used in this paper"--Federal Reserve Board web site.
Notes: Title from PDF file as viewed on 6/22/2009.

Includes bibliographical references.

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