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Deterministic vs. Stochastic Trend in U.S. GNP, Yet Again


  • Francis X. Diebold
  • Abdelhak S. Senhadji


A sleepy consensus has emerged that U.S. GNP data are uninformative as to whether trend is better described as deterministic or stochastic. Although the distinction is not critical in some contexts, it is important for point forecasting, because the two models imply very different long-run dynamics and hence different long-run forecasts. We argue that, even for the famously recalcitrant GNP series, unit root tests over long spans can be informative. Our results make clear that uncritical repetition of the `we don't know, and we don't care' mantra is just as scientifically irresponsible as blind adoption of the view that `all macroeconomic series are difference-stationary,' or the view that `all macroeconomic series are trend-stationary.' There is simply no substitute for serious, case- by-case analysis.

Suggested Citation

  • Francis X. Diebold & Abdelhak S. Senhadji, 1996. "Deterministic vs. Stochastic Trend in U.S. GNP, Yet Again," NBER Working Papers 5481, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:5481
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    References listed on IDEAS

    1. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Chapters,in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220 National Bureau of Economic Research, Inc.
    2. Stock, James H & Watson, Mark W, 1988. "Variable Trends in Economic Time Series," Journal of Economic Perspectives, American Economic Association, vol. 2(3), pages 147-174, Summer.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. DeJong, David N & Whiteman, Charles H, 1991. "The Case for Trend-Stationarity Is Stronger Than We Thought," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 413-421, Oct.-Dec..
    5. Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October.
    6. Christiano, Lawrence J. & Eichenbaum, Martin, 1990. "Unit roots in real GNP: Do we know, and do we care?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 32(1), pages 7-61, January.
    7. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    8. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
    9. Rudebusch, Glenn D, 1993. "The Uncertain Unit Root in Real GNP," American Economic Review, American Economic Association, vol. 83(1), pages 264-272, March.
    10. Ng, S. & Perron, P., 1994. "Unit Root Tests ARMA Models with Data Dependent Methods for the Selection of the Truncation Lag," Cahiers de recherche 9423, Universite de Montreal, Departement de sciences economiques.
    11. Balke, Nathan S & Gordon, Robert J, 1989. "The Estimation of Prewar Gross National Product: Methodology and New Evidence," Journal of Political Economy, University of Chicago Press, vol. 97(1), pages 38-92, February.
    12. Stock, James H. & Watson, Mark W., 1986. "Does GNP have a unit root?," Economics Letters, Elsevier, vol. 22(2-3), pages 147-151.
    13. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
    14. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
    15. Zelhorst, Dick & de Haan, Jakob, 1994. "The Nonstationarity of Aggregate Output: Some Additional International Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(1), pages 23-33, February.
    16. Hall, Alastair R, 1994. "Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 461-470, October.
    17. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    18. Graham Elliott, 1998. "On the Robustness of Cointegration Methods when Regressors Almost Have Unit Roots," Econometrica, Econometric Society, vol. 66(1), pages 149-158, January.
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    Cited by:

    1. Guillaume Chevillon, 2004. "`Weak` trends for inference and forecasting in finite samples," Economics Series Working Papers 210, University of Oxford, Department of Economics.
    2. Zarnowitz, Victor & Ozyildirim, Ataman, 2006. "Time series decomposition and measurement of business cycles, trends and growth cycles," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1717-1739, October.
    3. Prodan, Ruxandra, 2008. "Potential Pitfalls in Determining Multiple Structural Changes With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 50-65, January.
    4. Freitas, Paulo S.A. & Rodrigues, Antonio J.L., 2006. "Model combination in neural-based forecasting," European Journal of Operational Research, Elsevier, vol. 173(3), pages 801-814, September.
    5. Junsoo Lee & John List, 2004. "Examining Trends of Criteria Air Pollutants: Are the Effects of Governmental Intervention Transitory?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 29(1), pages 21-37, September.
    6. Corradi, Valentina & Swanson, Norman R., 2006. "The effect of data transformation on common cycle, cointegration, and unit root tests: Monte Carlo results and a simple test," Journal of Econometrics, Elsevier, vol. 132(1), pages 195-229, May.

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    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles


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