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.
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