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1. The Taylor Rule and Interval Forecast For Exchange Rates

Author:
Jian Wang and Jason J. Wu
Publication Date:
01-2009
Content Type:
Working Paper
Institution:
Board of Governors of the Federal Reserve System
Abstract:
This paper attacks the Meese-Rogoff (exchange rate disconnect) 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 Semi-parametric (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.
Topic:
Economics, Exchange Rate Policy, and Models
Political Geography:
Global Focus

2. Uncertainty over Models and Data: The Rise and Fall of American Inflation

Author:
Seth Pruitt
Publication Date:
12-2008
Content Type:
Working Paper
Institution:
Board of Governors of the Federal Reserve System
Abstract:
An economic agent who is uncertain of her economic model learns, and this learning is sensi- tive to the presence of data measurement error. I investigate this idea in an existing framework that describes the Federal Reserve’s role in U.S. inflation. This framework successfully fits the observed inflation to optimal policy, but fails to motivate the optimal policy by the perceived Philips curve trade-off between inflation and unemployment. I modify the framework to account for data uncertainty calibrated to the actual size of data revisions. The modified framework ameliorates the existing problems by adding sluggishness to the Federal Reserve’s learning: the key point is that data uncertainty is amplified by the nonlinearity induced by learning. Conse- quently there is an explanation for the rise and fall in inflation: the concurrent rise and fall in the perceived Philips curve trade-off.
Topic:
Economics, Inflation, and Models
Political Geography:
United States and North America