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Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches

  • de Silva, Ashton J

Innovations state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool. These models for the first time are applied to Australian macroeconomic data. In addition new multivariate specifications are outlined and demonstrated to be accurate.

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File URL: https://mpra.ub.uni-muenchen.de/27411/1/MPRA_paper_27411.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 27411.

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Date of creation: 13 Dec 2010
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Handle: RePEc:pra:mprapa:27411
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  1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
  2. Summers, Peter M., 2001. "Forecasting Australia's economic performance during the Asian crisis," International Journal of Forecasting, Elsevier, vol. 17(3), pages 499-515.
  3. 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 321-42, December.
  4. de Silva, Ashton, 2007. "A multivariate innovations state space Beveridge Nelson decomposition," MPRA Paper 5431, University Library of Munich, Germany.
  5. Sarantis Tsiaplias & Chew Lian Chua, 2008. "Forecasting Australian Macroeconomic Variables Using a Large Dataset," Melbourne Institute Working Paper Series wp2008n04, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  6. Muhammad Akram & Rob J Hyndman & J. Keith Ord, 2008. "Exponential smoothing and non-negative data," Working Papers 2008-003, The George Washington University, Department of Economics, Research Program on Forecasting.
  7. 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 9/00, Monash University, Department of Econometrics and Business Statistics.
  8. Adams, Philip D. & Dixon, Peter B. & McDonald, Daina & Meagher, G. A. & Parmenter, Brian R., 1994. "Forecasts for the Australian economy using the MONASH model," International Journal of Forecasting, Elsevier, vol. 10(4), pages 557-571, December.
  9. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  10. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
  11. Leu, Shawn Chen-Yu & Sheen, Jeffrey, 2011. "A small New Keynesian state space model of the Australian economy," Economic Modelling, Elsevier, vol. 28(1), pages 672-684.
  12. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
  13. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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