Forecast Selection by Conditional Predictive Ability Tests:

An Application to the Yen/Dollar Exchange Rate

January 2008
Kei Kawakami^*1

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Abstract

In this paper, I propose a new method for forecast selection from a pool of many forecasts. My method has two features. The first is the use of the conditional predictive ability test proposed by Giacomini and White [2006]. Second, I construct a measure with two dimensions: "relative usefulness" and "signal predictability". The measure is designed to rank many forecasts in the order of ex-ante forecast accuracy. Therefore, the ranking can be useful not only for selection of a single forecast but also for forecast combinations. I apply the method to the monthly yen/dollar exchange rate. First, I evaluate the performance of base-line forecasting models including a forecast survey of Japanese companies. Second, I show empirically that my method of switching forecasting models reduces forecast errors compared with a single model.

I would like to thank Raffaella Giacomini, Mototsugu Shintani, Tomoyoshi Yabu and the staff of the Bank of Japan for their helpful comments. The opinions expressed here, as well as any remaining errors, belong to the author and should not be ascribed to the Bank of Japan or the Monetary Affairs Department.

• *1 UCLA (formerly Monetary Affairs Department, Bank of Japan)
  E-mail: kei@ucla.edu

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