Demystifying the Meese-Rogoff Puzzle

by Moosa, Imad A.; Burns, Kelly

• ISBN13: 9781137452474
• ISBN10: 1137452471
• Format: Hardcover
• Copyright: 2014-12-17
• Publisher: Palgrave Pivot

Summary

For the past 30 years international monetary economists have believed that exchange rate models cannot outperform the random walk in out-of-sample forecasting as a result of the 1983 paper written by Richard Meese and Kenneth Rogoff.

Marking the culmination of their extensive research into the Meese-Rogoff puzzle, Moosa and Burns challenge the orthodoxy by demonstrating that the naïve random walk model can be outperformed by exchange rate models when forecasting accuracy is measured by metrics that do not rely exclusively on the magnitude of forecasting error. The authors present compelling evidence, supported by their own measure: the 'adjusted root mean square error', to finally solve the Meese-Rogoff puzzle and provide a new alternative.

Demystifying the Meese-Rogoff Puzzle will appeal to academics with an interest in exchange rate economics and international monetary economics. It will also be a useful resource for central banks and financial institutions.

Author Biography

Imad Moosa is Professor of Finance at the Royal Melbourne Institute of Technology (RMIT), Australia. He has also held previous appointments at Monash and La Trobe Universities, Australia, and the University of Sheffield, UK. Prior to working in academia he held professional positions at the International Monetary Fund and in investment banking. He has published 16 books and over 200 papers.

Kelly Burns is a Research Fellow at Curtin Graduate School of Business, Australia. She has held previous appointments at the Australian Bureau of Statistics, the Department of Premier and Cabinet, the Department of Justice, and the Colonial First State. In 2002, she graduated from La Trobe University, Australia, and was the recipient of the Dean's Medal for Economics.

Table of Contents

1. The Meese-Rogoff Puzzle
2. A Selective Survey of Subsequent Studies
3. Basic Methodology, Data and Results
4. Alternative Measures of Forecasting Accuracy
5. Stochastic Movements in the Underlying Parameters
6. Model Misspecification
7. The Effect of Non-linearities
8. Simultaneous Equation Bias
9. Sampling Errors
10. Modelling Expectations
11. Concluding Remarks