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Prediction and Simulation Using Simple Models Characterized by Nonstationarity and Seasonality

Listed author(s):
  • Norman Swanson

    ()

    (Rutgers University)

  • Richard Urbach

    (Conning Germany Gmbh)

In this paper, we provide new evidence on the empirical usefulness of various simple seasonal models, and underscore the importance of carefully designing criteria by which one judges alternative models. In particular, we underscore the importance of both choice of forecast or simulation horizon and choice between minimizing point or distribution based loss measures. Our empirical analysis centers around the implementation of a series of simulation and prediction experiments, as well as a discussion of the stochastic properties of seasonal unit root models. Our prediction experiments are based on analysis of a group of 14 variables have been chosen to closely mimic the set of indicators used by the Federal Reserve to help in setting U.S. monetary policy, and our simulation experiments are based on a comparison of simulated and historical distributions of said variables using the testing approach of Corradi and Swanson (2007a).

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File URL: http://www.sas.rutgers.edu/virtual/snde/wp/2013-23.pdf
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Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201323.

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Length: 20 pages
Date of creation: 10 Aug 2013
Handle: RePEc:rut:rutres:201323
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  1. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
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  13. Corradi, Valentina & Swanson, Norman R., 2011. "Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models," Journal of Econometrics, Elsevier, vol. 161(2), pages 304-324, April.
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  17. repec:hal:journl:peer-00796745 is not listed on IDEAS
  18. Paulo Rodrigues & Denise Osborn, 1999. "Performance of seasonal unit root tests for monthly data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 985-1004.
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  25. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
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  27. Corradi, Valentina & Swanson, Norman R., 2007. "Evaluation of dynamic stochastic general equilibrium models based on distributional comparison of simulated and historical data," Journal of Econometrics, Elsevier, vol. 136(2), pages 699-723, February.
  28. Zacharias Psaradakis, 1997. "Testing for unit roots in time series with nearly deterministic seasonal variation," Econometric Reviews, Taylor & Francis Journals, vol. 16(4), pages 421-439.
  29. Franses, Philip Hans & van Dijk, Dick, 2005. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," International Journal of Forecasting, Elsevier, vol. 21(1), pages 87-102.
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  36. Bell, William, 1987. "A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models with Monthly Means and Models with (0, 1, 1)12 Seasonal Parts when Theta=1," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 383-387, July.
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