Testing Long-horizon Predictive Ability with High Persistence, and the Meese-Rogoff Puzzle

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Testing Long-horizon Predictive Ability with High Persistence, and the Meese-Rogoff Puzzle

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Rossi, Barbara

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Barbara Rossi

Abstract

A well-known puzzle in the international finance literature is that a random walk predicts exchange rates better than economic models (Meese and Rogoff, 1983a, b and 1988). This paper offers a potential explanation for this finding. When exchange rates and fundamentals are highly persistent, long-horizon forecasts of economic models are biased by the estimation error in the parameter that measures the persistence. When this bias outweighs the benefits from exploiting economic information, the random walk model will forecast better. This happens even if the economic model is the true data generating process. The reason is that a random walk model imposes a unit root, rather than estimates it. The paper thus proposes a test for equal predictive ability in the presence of highly persistent variables. When applied to the Meese-Rogoff exercise, this test shows that the poor forecasting ability of economic models DOES NOT imply that the models are NOT a good description of the data.

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Paper provided by Duke University, Department of Economics in its series Working Papers with number 02-10.

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Date of creation: 2002
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Handle: RePEc:duk:dukeec:02-10

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Article
- Barbara Rossi, 2005. "Testing Long-Horizon Predictive Ability With High Persistence, And The Meese-Rogoff Puzzle," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(1), pages 61-92, 02. [Downloadable!] (restricted)

Find related papers by JEL classification:
F30 - International Economics - - International Finance - - - General
F40 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - General

This paper has been announced in the following NEP Reports:

NEP-ALL-2003-02-18 (All new papers)
NEP-ETS-2003-02-18 (Econometric Time Series)
NEP-IFN-2003-02-18 (International Finance)

Cited by:
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Erik Hjalmarsson, 2006. "New methods for inference in long-run predictive regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City. [Downloadable!]
Other versions:
- Todd E. Clark & Michael W. McCracken, 2008. "Tests of equal predictive ability with real-time data," Working Papers 2008-029, Federal Reserve Bank of St. Louis. [Downloadable!]
Jin Lee, 2005. "Long horizon regressions with moderate deviations from a unit root," Economics Bulletin, Economics Bulletin, vol. 3(52), pages 1-11. [Downloadable!]
Jian Wang & Jason J. Wu, 2008. "The Taylor rule and forecast intervals for exchange rates," Globalization and Monetary Policy Institute Working Paper 22, Federal Reserve Bank of Dallas. [Downloadable!]
Other versions:
- Jian Wang & Jason J. Wu, 2009. "The Taylor rule and forecast intervals for exchange rates," International Finance Discussion Papers 963, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
Valerie Cerra & Sweta Chaman Saxena, 2008. "The Monetary Model Strikes Back: Evidence from the World," IMF Working Papers 08/73, International Monetary Fund. [Downloadable!]
Chevillon, Guillaume, 2007. "Inference in the Presence of Stochastic and Deterministic Trends," ESSEC Working Papers DR 07021, ESSEC Research Center, ESSEC Business School. [Downloadable!]
Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics. [Downloadable!]
Other versions:
- Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578. [Downloadable!] (restricted)

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