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


  • 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)


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.

Suggested Citation

  • Jumah, Adusei & Kunst, Robert M., 2008. "Optimizing Time-series Forecasts for Inflation and Interest Rates Using Simulation and Model Averaging," Economics Series 231, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:231

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    References listed on IDEAS

    1. Armstrong, J. Scott, 2007. "Significance Tests Harm Progress in Forecasting," MPRA Paper 81664, University Library of Munich, Germany.
    2. 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 627-645, August.
    3. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.
    4. 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.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Frédérique Bec & Anders Rahbek & Neil Shephard, 2005. "The Autoregressive Conditional Root (ACR) Model," Working Papers 2005-26, Center for Research in Economics and Statistics.
    7. Anders Rahbek & Neil Shephard, 2001. "Autoregressive conditional root model," Economics Papers 2002-W7, Economics Group, Nuffield College, University of Oxford, revised 01 Feb 2002.
    8. 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 116-126, February.
    9. 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 1062-1088, October.
    10. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    11. Clive W. J. Granger, 2005. "Modeling, Evaluation, and Methodology in the New Century," Economic Inquiry, Western Economic Association International, vol. 43(1), pages 1-12, January.
    12. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    13. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, July.
    14. 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.
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    More about this item


    Threshold cointegration; Parametric bootstrap; Model averaging;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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