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Modeling trends

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  • Christopher A. Sims

Abstract

Models of low-frequency behavior of time series may have strongly conflicting substantive implications while fitting the data nearly equally well. We should develop methods which display the resulting uncertainty rather than adopt modeling conventions which hide it. One step toward this goal may be to consider overparameterized stationary ARMA models.

Suggested Citation

  • Christopher A. Sims, 1989. "Modeling trends," Discussion Paper / Institute for Empirical Macroeconomics 22, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmem:22
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    References listed on IDEAS

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    1. 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.
    2. John Y. Campbell & N. Gregory Mankiw, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(4), pages 857-880.
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    Cited by:

    1. Sims, Christopher A., 2000. "Using a likelihood perspective to sharpen econometric discourse: Three examples," Journal of Econometrics, Elsevier, vol. 95(2), pages 443-462, April.
    2. Lawrence J. Christiano, 1989. "P*: not the inflation forecaster's holy grail," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 13(Fall), pages 3-18.
    3. Weichun Chen & Judith A. Clarke & Nilanjana Roy, 2014. "Health and wealth: Short panel Granger causality tests for developing countries," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 23(6), pages 755-784, September.
    4. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 638-647, October.

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