Forecasting the Real Exchange Rates Behavior: An Investigation of Nonlinear Competing Models
A large amount of literature finds that real exchange rates appear to be characterized by several non-linear specifications. While each of these nonlinear models fits some particular real exchange rates especially well, leading to good in-sample properties, the recent studies have not come to any consensus whether the nonlinear models could provide a better specification than the linear model and/or the random walk model according to their out-of-sample forecasting performances. Our goal is to examine two important nonlinear models (Band-TAR and ESTAR) concerning their abilities to generate out-of-sample forecasts, when estimating real exchange rates for 20 OECD countries. We find strong evidence that the ESTAR model, but not the linear or the Band-TAR model, outperforms the random walk model when forecasting out-of-sample. On the other hand, a comparison between the nonlinear models and the linear model is inconclusive due to the low power of tests for predictive ability when bootstrapping critical values.
This document is currently not available here.