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Forecasting macroeconomic variables using a structural state space model

Author

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  • de Silva, Ashton

Abstract

This paper has a twofold purpose; the first is to present a small macroeconomic model in state space form, the second is to demonstrate that it produces accurate forecasts. The first of these objectives is achieved by fitting two forms of a structural state space macroeconomic model to Australian data. Both forms model short and long run relationships. Forecasts from these models are subsequently compared to a structural vector autoregressive specification. This comparison fulfills the second objective demonstrating that the state space formulation produces more accurate forecasts for a selection of macroeconomic variables.

Suggested Citation

  • de Silva, Ashton, 2008. "Forecasting macroeconomic variables using a structural state space model," MPRA Paper 11060, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:11060
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    File URL: https://mpra.ub.uni-muenchen.de/11060/1/MPRA_paper_11060.pdf
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    References listed on IDEAS

    as
    1. Daniel Buncic & Martin Melecky, 2008. "An Estimated New Keynesian Policy Model for Australia," The Economic Record, The Economic Society of Australia, vol. 84(264), pages 1-16, March.
    2. Tommaso PROIETTI, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Economics Working Papers ECO2002/23, European University Institute.
    3. Matheson, Troy, 2010. "Assessing the fit of small open economy DSGEs," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 906-920, September.
    4. Renee Fry & James Hocking & Vance L. Martin, 2008. "The Role of Portfolio Shocks in a Structural Vector Autoregressive Model of the Australian Economy," The Economic Record, The Economic Society of Australia, vol. 84(264), pages 17-33, March.
    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. Nathan S. Balke & Mark E. Wohar, 2002. "Low-Frequency Movements in Stock Prices: A State-Space Decomposition," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 649-667, November.
    7. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. Charles Nelson & Eric Zivot, 2000. "Why are Beveridge-Nelson and Unobserved-Component Decompositions of GDP so Different?," Econometric Society World Congress 2000 Contributed Papers 0692, Econometric Society.
    10. Leon Berkelmans, 2005. "Credit and Monetary Policy: An Australian SVAR," RBA Research Discussion Papers rdp2005-06, Reserve Bank of Australia.
    11. Morley, James C., 2002. "A state-space approach to calculating the Beveridge-Nelson decomposition," Economics Letters, Elsevier, vol. 75(1), pages 123-127, March.
    12. Dungey, Mardi & Pagan, Adrian, 2000. "A Structural VAR Model of the Australian Economy," The Economic Record, The Economic Society of Australia, vol. 76(235), pages 321-342, December.
    13. James C. Morley & Charles R. Nelson & Eric Zivot, 2003. "Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 235-243, May.
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    Cited by:

    1. Katsuyuki Shibayama, 2015. "Trend Dominance in Macroeconomic Fluctuations," Studies in Economics 1518, School of Economics, University of Kent.

    More about this item

    Keywords

    State space; multivariate time series; macroeconomic model; forecast; SVAR;

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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