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Forecasting Australian Macroeconomic Variables Using a Large Dataset

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  • Sarantis Tsiaplias

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Chew Lian Chua

Abstract

This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches. JEL Classification: C22; C53; E17

Suggested Citation

  • 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.
  • Handle: RePEc:iae:iaewps:wp2008n04
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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2008n04.pdf
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    References listed on IDEAS

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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    4. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    5. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    6. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    7. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    8. David A. Bessler & John L. Kling, 1986. "Forecasting Vector Autoregressions with Bayesian Priors," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(1), pages 144-151.
    9. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 664-671, November.
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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.
    2. Guay C. Lim & Chew Lian Chua & Edda Claus & Sarantis Tsiaplias, 2010. "Review of the Australian Economy 2009-10: On the Road to Recovery," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 43(1), pages 1-11.

    More about this item

    Keywords

    PDiffusion indexes; Forecasting; Australia.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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