A reconsideration of the Meese-Rogoff puzzle
Creators: name: Burns, Kelly
Publisher: name: RMIT University
Year: 2024

description: In 1983, Meese and Rogoff published empirical results concluding that the naïve random walk model cannot be outperformed in out-of-sample forecasting. Over the past 30 years, a comprehensive body of literature dedicated to unravelling what is now known as the Meese-Rogoff “puzzle” has been unsuccessful in overturning these results. It is now widely accepted that exchange rate models cannot outperform a naïve random walk model in out-of-sample forecasting. While this conclusion has continued to puzzle economists over the years, a robust explanation has yet to be found, and the results are yet to be overturned.<br><br>The importance of reconsidering this puzzle is threefold. First, the puzzle challenges the foundations of international macroeconomics. Second, the puzzle stands in stark contrast to commercial reality, where profit maximising firms pay for professionally generated forecasts, rather than utilise the costless random walk forecasts. Third, resolving the puzzle has become increasingly important in the contemporary economic environment where a better understanding of exchange rate movements is vital for business and policy makers.<br><br>The purpose of this project is to reconsider the Meese-Rogoff puzzle by comprehensively reviewing the reasons suggested in the literature for the superior performance of the naïve random walk model. This includes simultaneous equations bias, sampling errors, stochastic movements in the model parameters, model misspecification, failure to account for non-linearities and improper modelling of expectations. This project is distinguishable from previous work because it considers whether assessing forecasting accuracy exclusively by the magnitude of forecasting error could explain the puzzle. This as a possible reason for the Meese-Rogoff puzzle is mostly overlooked in the literature. <br><br>This project demonstrates that the naïve random walk model cannot be outperformed by exchange rate models in terms of the magnitude of forecasting error. Incorporating stochastic movements in underlying parameters did improve the forecasting accuracy of the model. However, the model could not produce a numerically smaller magnitude of error that was also statistically different to that of the random walk. Consideration of sampling error, model misspecification and modelling of expectations yields similar results. The results from addressing simultaneous equation bias and non-linearities are discounted on the basis that the model specification introduces a random walk component. In contrast, exchange rate models outperform the random walk when forecasting accuracy is assessed by measures that do not exclusively rely on the magnitude of error. This includes direction accuracy, proximity to perfect forecast and measures that take into account magnitude and direction of error. <br><br>The Meese-Rogoff findings should not be considered a “puzzle”. Exchange rate models are “black box” processes and cannot explain how exchange rate volatility is driven by different expectations about macroeconomic fundamentals through order flows. Although the findings firmly establish that the naïve model is unbeatable in terms of the magnitude of forecasting error, this does not mean the random walk produces a superior forecast. The proposition that the random walk cannot be outperformed by the monetary models is overturned when forecasting accuracy is assessed by alternative measures. <br>