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A multivariate innovations state space Beveridge Nelson decomposition

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  • de Silva, Ashton

Abstract

The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that this new speci¯cation is more simple than conventional state space and cointegration approaches. The approach is illustrated using a trivariate data set comprising the GD(N)P of Australia, America and the UK.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 5431.

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Date of creation: Oct 2007
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Handle: RePEc:pra:mprapa:5431

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Keywords: vector innovation structural time series; multivariate time series; Bev- eridge Nelson; common components;

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References

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  1. Chin Nam Low & Heather Anderson & Ralph Snyder, 2004. "Single Source of Error State Space Approach to the Beveridge Nelson Decomposition," Econometric Society 2004 Australasian Meetings, Econometric Society 242, Econometric Society.
  2. Stock, James H & Watson, Mark W, 1988. "Variable Trends in Economic Time Series," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 2(3), pages 147-74, Summer.
  3. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, American Economic Association, vol. 81(4), pages 819-40, September.
  4. 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.
  5. Francisco Barillas & Christoph Schleicher, 2003. "Common Trends and Common Cycles in Canadian Sectoral Output," Working Papers, Bank of Canada 03-44, Bank of Canada.
  6. Robert F. Engle & João Victor Issler, 1993. "Common trends and common cycles in Latin America," Revista Brasileira de Economia, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 47(2), pages 149-176, April.
  7. 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, Monash University, Department of Econometrics and Business Statistics 3/07, Monash University, Department of Econometrics and Business Statistics.
  8. James Morley & Charles Nelson & Eric Zivot, 2003. "Why are Beveridge-Nelson and Unobserved-component decompositions of GDP so Different?," Working Papers, University of Washington, Department of Economics UWEC-2002-18-P, University of Washington, Department of Economics.
  9. 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.
  10. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics, EconWPA 0209002, EconWPA.
  11. Vahid, F & Engle, Robert F, 1993. "Common Trends and Common Cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 8(4), pages 341-60, Oct.-Dec..
  12. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198774501, October.
  13. Vahid, Farshid & Engle, Robert F., 1997. "Codependent cycles," Journal of Econometrics, Elsevier, Elsevier, vol. 80(2), pages 199-221, October.
  14. Hyndman, R.J. & Koehler, A.B. & Snyder, R.D. & Grose, S., 2000. "A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 9/00, Monash University, Department of Econometrics and Business Statistics.
  15. 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.
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Cited by:
  1. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.

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