Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors

We propose factor-based out of sample forecasting models for US dollar real exchange rates. We estimate latent common factors employing an array of data dimensionality reduction approaches that include the Principal Component Analysis, Partial Least Squares, and the LASSO for a large panel of 125 monthly frequency US macroeconomic time series data. We augment two benchmark models, a stationary autoregressive model and the random walk model, with estimated common factors to formulate out-of-sample forecasts of the real exchange rate. Empirical findings demonstrate that our factor augmented models outperform the benchmark models at longer horizons when factors are extracted from real activity variables excluding financial sector variables. Factors obtained from financial market variables overall play a limited role in forecasting. Our data-driven models tend to perform better than models with international factors that are motivated by exchange rate determination theories.