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

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  • Diebold
  • Senhadji

Abstract

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.
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  • Diebold & Senhadji, "undated". "Deterministic vs. Stochastic Trend in U.S. GNP, Yet Again," Home Pages _054, University of Pennsylvania.
    • Handle: RePEc:wop:pennhp:_054
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    References listed on IDEAS

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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. 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.
    3. Foster, Neil & Stehrer, Robert, 2007. "Modeling transformation in CEECs using smooth transitions," Journal of Comparative Economics, Elsevier, vol. 35(1), pages 57-86, March.
    4. 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.
    5. Tortorice, Daniel L, 2018. "The business cycle implications of fluctuating long run expectations," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 266-291.
    6. 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.
    7. 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.
    8. 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.

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    More about this item

    JEL classification:

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

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