Abstract
We examine a class of popular structural models of exchange rate determination and compare them to a random walk with and without drift. Given almost any set of conditioning variables, we find parametric specifications fail. Our findings are based on a broad entropy function of the whole distribution of variables and forecasts. We also find significant evidence of nonlinearity and/or “higher moment” influences which seriously questions the habit of forecast and model evaluation based on mean-variance criteria. Taylor rule factors may improve out of sample “forecasts” for some models and exchanges, but do not offer similar improvement for in-sample (historical) fit. We estimate models of exchange rate determination nonparametrically so as to avoid functional form issues. Taylor rule and some other variables are smoothed out, being statistically irrelevant in sample. The metric entropy tests suggest significant differences between the observed densities and their in- and out- of sample forecasts and fitted values. Much like the Diebold-Mariano approach, we are able to report statistical significance of the differences with our more general measures of forecast performance.
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Notes
- 1.
It might be interesting to evaluate the performance of the “hybrid” model by using the metric entropy criterion, but we leave that to future studies.
- 2.
The detailed information about the structural models and the out-of-sample forecasting methodology will be provided in the following section.
- 3.
- 4.
Variables in parentheses denote the foreign country counterparts.
- 5.
See Molodtsova and Papell (2009) and Wang and Wu (2010) for the derivation of the models. A specification search approach to these models may be a worthy topic of research. The appropriate approach in that setting would be the data snooping techniques proposed by White (2000) in which no model may be correctly specified. This realism is an enduring aspect of techniques developed by Hal White. The object of inference in such settings would be the “pseudo parameters” which are afforded a compelling and clear definition based on entropy concepts such as the ones employed in this chapter.
- 6.
Rogoff and Stavrakeva (2008) argue that CW test statistics cannot be used to evaluate forecasting performance as it is not testing the null of equal predictive accuracy, hence they suggest to use bootstrapped critical values. There is less evidence in favor of Taylor-rule based models when CW test statistics with bootstrapped critical values are used.
- 7.
Metric entropy measurements are done in R by using the np package (Hayfield and Racine (2008))
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Maasoumi, E., Bulut, L. (2013). Predictability and Specification in Models of Exchange Rate Determination. In: Chen, X., Swanson, N. (eds) Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1653-1_16
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