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Optimizing Time-series Forecasts for Inflation and Interest Rates Using Simulation and Model Averaging

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Author Info
Jumah, Adusei (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Department of Economics, University of Vienna, Vienna, Austria)
Kunst, Robert M. (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Department of Economics, University of Vienna, Vienna, Austria)

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Abstract

Motivated by economic-theory concepts—the Fisher hypothesis and the theory of the term structure—we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR, and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK, and the U.S. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models.

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File URL: http://www.ihs.ac.at/publications/eco/es-231.pdf
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Publisher Info
Paper provided by Institute for Advanced Studies in its series Economics Series with number 231.

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Length: 25 pages
Date of creation: Nov 2008
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Handle: RePEc:ihs:ihsesp:231

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Related research
Keywords: Threshold cointegration; Parametric bootstrap; Model averaging;

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Determination of Interest Rates; Term Structure of Interest Rates
E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation

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References listed on IDEAS
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  1. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253. [Downloadable!] (restricted)
  2. Malliaropulos, Dimitrios, 2000. "A note on nonstationarity, structural breaks, and the Fisher effect," Journal of Banking & Finance, Elsevier, vol. 24(5), pages 695-707, May. [Downloadable!] (restricted)
  3. Balke, Nathan S & Fomby, Thomas B, 1997. "Threshold Cointegration," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(3), pages 627-45, August.
    Other versions:
  4. Campbell, John Y & Shiller, Robert J, 1987. "Cointegration and Tests of Present Value Models," Journal of Political Economy, University of Chicago Press, vol. 95(5), pages 1062-88, October. [Downloadable!] (restricted)
    Other versions:
  5. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, 07. [Downloadable!] (restricted)
  6. Hall, Anthony D & Anderson, Heather M & Granger, Clive W J, 1992. "A Cointegration Analysis of Treasury Bill Yields," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 116-26, February. [Downloadable!] (restricted)
  7. Adusei Jumah & Robert M. Kunst, 2008. "Seasonal prediction of European cereal prices: good forecasts using bad models?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 391-406. [Downloadable!]
  8. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  9. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327. [Downloadable!] (restricted)
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