The Beveridge-Nelson Decomposition in Retrospect and Prospect
AbstractBeveridge and Nelson [Beveridge, Stephen, Nelson, Charles R., 1981. A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the 'business cycle'. Journal of Monetary Economics 7, 151-174] proposed that the long-run forecast is a measure of trend for time series such as GDP that do not follow a deterministic path in the long run. They showed that if the series is stationary in first differences, then the estimated trend is a random walk with drift that accounts for growth, and the cycle is stationary. In contrast to linear de-trending, the smoother of Hodrick and Prescott (1981) and Hodrick and Prescott [Hodrick, Robert, Prescott, Edward C., 1997. Post-war US business cycles: An empirical investigation. Journal of Money Credit and Banking 29 (1), 1-16] and the unobserved components model of Harvey, [Harvey, A.C., 1985. Trends and cycles in macroeconomic time series. Journal of Business and Economic Statistics 3, 216-227]. Watson [Watson, Mark W., 1986. Univariate detrending methods with stochastic trends Journal of Monetary Economics 18, 49-75] and Clark [Clark, Peter K., 1987. The cyclical component of US economic activity. The Quarterly Journal of Economics 102 (4), 797-814], the BN decomposition attributes most variation in GDP to trend shocks while the cycles are short and brief. Since each is an estimate of the transitory part of GDP that will die out, it seems natural to compare cycle measures by their ability to forecast future growth. The results presented here suggest that cycle measures contain little if any information beyond the short-term momentum captured by BN.
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Bibliographic InfoPaper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2007-30.
Date of creation: Aug 2006
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Other versions of this item:
- Nelson, Charles R., 2008. "The Beveridge-Nelson decomposition in retrospect and prospect," Journal of Econometrics, Elsevier, vol. 146(2), pages 202-206, October.
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