de Silva, Ashton J (2010): Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches.
Download (94kB) | Preview
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.
|Item Type:||MPRA Paper|
|Original Title:||Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches|
|Keywords:||exponential smoothing, state space models, multivariate time series, macroeconomic variables|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications
|Depositing User:||Ashton de Silva|
|Date Deposited:||16. Dec 2010 14:18|
|Last Modified:||15. Feb 2013 19:35|
Adams, P. D., Dixon, P. B., McDonald, D., Meagher, G. A. & Parmenter, B. R. (1994), ‘Forecasts for the australian economy using the monash model’, International Journal of Forecasting 10(4), 557 – 571.
Akram, M., Hyndman, R. J. & Ord, J. K. (2009), ‘Exponential smoothing and non-negative data’, Australian and New Zealand Journal of Statistics 51(4), 415–432.
Anderson, B. & Moore, J. B. (1979), Optimal Filtering, Prentice-Hall.
Aoki, M. & Havenner, A. (1991), ‘State space modeling of multiple time series’, Econometric Reviews 10, 1–59.
Davidson, S. & de Silva, A. (2009), ‘Unemployment more than just a number’, Crikey. URL: http://www.crikey.com.au/2009/10/09/unemployment-more-than-just-a-number/
de Silva, A., Hyndman, R. & Snyder, R. (2009), ‘A multivariate innovations state space Beveridge Nelson decomposition’, Economic Modelling 26(5), 1067–1074.
de Silva, A., Hyndman, R. & Snyder, R. (2010), ‘The vector innovation structural times series framework: a simple approach to multivariate forecasting.’, International Journal of Statistical Modelling p. Forthcoming.
Fildes, R. (2001), ‘Beyond forecasting competitions’, International Journal of Forecasting 17, 556 560.
Gardner, E. S. (2006), ‘Exponential smoothing: The state of the art—Part II’, International Journal of Forecasting 22, 637–666.
Harvey, A. C. (1989), Forecasting, structural time series models and the Kalman filter, Cambridge University Press, Cambridge.
Hyndman, R. J. & Khandakar, Y. (2008), ‘Automatic time series forecasting: the forecast package for r’, Journal of Statistical Software 27.
Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008), Forecasting with exponential smoothing: the state space approach, Springer.
Hyndman, R. J., Koehler, A. B., Snyder, R. D. & Grose, S. (2002), ‘A state space framework for automatic forecasting using exponential smoothing methods’, International Journal of Forecasting 18, 439–454.
Leu, S. C.-Y. & Sheen, J. (2007), A small state-space model of the australian economy, Technical report, Department of Economics, Macquarie University.
Makridakis, S. & Hibon, M. (2000), ‘The m3-competition: results, conclusions and implications’, International Journal of Forecasting 16, 451–476.
Meese, R. & Rognoff, K. (1983), ‘Empirical exchange rate model of the seventies’, Journal of International Economics 14, 3–24.
Pagan, A. & Dungey, M. (2000), ‘A Structural VAR Model of the Australian Economy’, Economic Record 76, 321–342.
Panher, G. S. (2007), ‘Modelling and controlling monetary and economic identities with constrained state space models’, International Statistical Reveiw 75, 150–169.
R Development Core Team (2009), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. URL: http://www.R-project.org
Stock, J. H. & Watson, M. (2002a), ‘Forecasting using principal components from a large number of predictors’, Journal of the American Statistical Association 97, 1167–1179.
Stock, J. H. & Watson, M. W. (2002b), ‘Macroeconomic forecasting using diffusion indexes’, Journal of Business and Economic Statistics 20, 147–162.14
Summers, P. M. (1999), ‘Macroeconomic forecasting at the Melbourne institute’, The Australian Economic Review 32, 197–205.
Summers, P. M. (2001), ‘Forecasting australia’s economic performance during the Asian crisis’, International Journal of Forecasting 17(3), 499 – 515.
Taylor, J. (2003), ‘Exponential smoothing with a damped multiplicative trend’, International Journal of Forecasting 19, 715–725.
Tsiaplias, S. & Chua, C. (2010), ‘Forecasting australian macroeconomic variables using a large dataset’, Australian Economic Papers 49, 44–59.