It’s a pleasure to have an opportunity to discuss this stimulating paper by Bachetta, van Wincoop and Beutler. This paper represents a part of an innovative research agenda investigating the reasons why structural macroeconomic models that economists use to explain exchange rates have so much difficulty in outpredicting a random walk, even when using contemporaneous information on the “fundamentals”. In discussing this paper, I’ll first review what other researchers have tried, recap quickly what is undertaken in this paper, and discuss why we should expect time variation in the parameter values. Then I’ll provide some insight into why one might expect different types of time variation, not considered by the authors, to be relevant. Previous Attempts to Overturn the Meese-Rogoff Results As the authors note, the Meese-Rogoff papers sparked an enormous literature. Various authors attempted to overturn the finding that in out-of-sample forecasting exercises using actually realized (as opposed to forecasted) values of the right hand side variables. As it turned out, no structural models of the era outperformed the random walk. This set of models included the monetary model, portfolio balance models incorporating cumulated current account balances. In addition, the forward rate, and simple time series models were included. In essentially no case did these alternative models outperform a random walk along the mean squared error or mean absolute error dimensions. Cheung, et al (2005) represents one of the most recent updates to the Meese-Rogoff papers.