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Forecasting macroeconomic variables using a structural state space model

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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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11060.

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Date of creation: 01 Sep 2008
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Handle: RePEc:pra:mprapa:11060

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

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  1. Morley, James C., 2002. "A state-space approach to calculating the Beveridge-Nelson decomposition," Economics Letters, Elsevier, Elsevier, vol. 75(1), pages 123-127, March.
  2. 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.
  3. Martin Melecky & Daniel Buncic, 2005. "An Estimated, New Keynesian Policy Model for Australia," Macroeconomics, EconWPA 0511026, EconWPA.
  4. James Morley & Charles Nelson & Eric Zivot, 2002. "Why Are Beveridge-Nelson and Unobserved-Component Decompositions of GDP So Different?," Working Papers, University of Washington, Department of Economics UWEC-2002-01, University of Washington, Department of Economics.
  5. 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, The Economic Society of Australia, vol. 84(264), pages 17-33, 03.
  6. Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 13/05, Monash University, Department of Econometrics and Business Statistics.
  7. Nathan S. Balke & Mark E. Wohar, 2001. "Low frequency movements in stock prices: a state space decomposition," Working Papers, Federal Reserve Bank of Dallas 0001, Federal Reserve Bank of Dallas.
  8. Troy Matheson, 2006. "Assessing the fit of small open economy DSGEs," Reserve Bank of New Zealand Discussion Paper Series DP2006/11, Reserve Bank of New Zealand.
  9. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 13(3), pages 253-63, July.
  10. Leon Berkelmans, 2005. "Credit and Monetary Policy: An Australian SVAR," RBA Research Discussion Papers, Reserve Bank of Australia rdp2005-06, Reserve Bank of Australia.
  11. Dungey, Mardi & Pagan, Adrian, 2000. "A Structural VAR Model of the Australian Economy," The Economic Record, The Economic Society of Australia, The Economic Society of Australia, vol. 76(235), pages 321-42, December.
  12. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics, EconWPA 0209002, EconWPA.
  13. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 8(3), pages 231-47, July-Sept.
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