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A multivariate innovations state space Beveridge-Nelson decomposition

  • de Silva, Ashton
  • Hyndman, Rob J.
  • Snyder, Ralph

The Beveridge-Nelson vector innovations structural time series framework is a new formulation that decomposes a set of variables into their permanent and transitory components. The proposed framework is flexible, modelling inter-series relationships and common features in a simple manner. In particular, it is shown that this new specification is simpler than conventional state space and cointegration approaches. The approach is illustrated using a trivariate data set comprising the GDP of Australia, the USA and the UK.

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Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 26 (2009)
Issue (Month): 5 (September)
Pages: 1067-1074

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Handle: RePEc:eee:ecmode:v:26:y:2009:i:5:p:1067-1074
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/30411

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  1. James Morley & Charles Nelson & Eric Zivot, 2003. "Why are Beveridge-Nelson and Unobserved-component decompositions of GDP so Different?," Working Papers UWEC-2002-18-P, University of Washington, Department of Economics.
  2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
  3. Heather M. Anderson & Chin Nam Low & Ralph Snyder, 2004. "Single Source of Error State Space Approach to the Beveridge Nelson Decomposition," Monash Econometrics and Business Statistics Working Papers 21/04, Monash University, Department of Econometrics and Business Statistics.
  4. Christoph Schleicher & Francisco Barillas, 2005. "Common Trends and Common Cycles in Canadian Sectoral Output," Computing in Economics and Finance 2005 214, Society for Computational Economics.
  5. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-40, September.
  6. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics 0209002, EconWPA.
  7. Vahid, F & Engle, Robert F, 1993. "Common Trends and Common Cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 341-60, Oct.-Dec..
  8. 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 231-47, July-Sept.
  9. Morley, James C., 2002. "A state-space approach to calculating the Beveridge-Nelson decomposition," Economics Letters, Elsevier, vol. 75(1), pages 123-127, March.
  10. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
  11. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
  12. Stock, James H & Watson, Mark W, 1988. "Variable Trends in Economic Time Series," Journal of Economic Perspectives, American Economic Association, vol. 2(3), pages 147-74, Summer.
  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.
  14. Vahid, Farshid & Engle, Robert F., 1997. "Codependent cycles," Journal of Econometrics, Elsevier, vol. 80(2), pages 199-221, October.
  15. repec:fgv:epgrbe:v:47:n:2:a:1 is not listed on IDEAS
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