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Forecasting with Factors: The Accuracy of Timeliness

Author

Listed:
  • Christian Gillitzer

    (Reserve Bank of Australia)

  • Jonathan Kearns

    (Reserve Bank of Australia)

Abstract

This paper demonstrates that factor-based forecasts for key Australian macroeconomic series can outperform standard time-series benchmarks. In practice, however, the advantages of using large panels of data to construct the factors typically comes at the cost of using less timely series, thereby delaying when the forecasts can be made. To produce more timely forecasts it is possible to use a narrower data panel, though this will possibly result in less accurate factor estimates and so less accurate forecasts. We demonstrate this trade-off between accuracy and timeliness with out-of-sample forecasts. With the exception of only consumer price inflation, the forecasts do not become less accurate as they utilise less information by excluding less timely series. So while factor forecasts have large data requirements, we show that these should not prevent their practical use when timely forecasts are needed.

Suggested Citation

  • Christian Gillitzer & Jonathan Kearns, 2007. "Forecasting with Factors: The Accuracy of Timeliness," RBA Research Discussion Papers rdp2007-03, Reserve Bank of Australia.
  • Handle: RePEc:rba:rbardp:rdp2007-03
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    File URL: https://www.rba.gov.au/publications/rdp/2007/pdf/rdp2007-03.pdf
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    References listed on IDEAS

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    1. Elena Angelini & Jérôme Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," BIS Papers chapters,in: Bank for International Settlements (ed.), Empirical studies of structural changes and inflation, volume 3, pages 109-138 Bank for International Settlements.
    2. 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.
    3. Martin Schneider & Martin Spitzer, 2004. "Forecasting Austrian GDP using the generalized dynamic factor model," Working Papers 89, Oesterreichische Nationalbank (Austrian Central Bank).
    4. Scott Brave & Jonas D. M. Fisher, 2004. "In search of a robust inflation forecast," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q IV, pages 12-31.
    5. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    6. 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.
    7. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
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    Cited by:

    1. Hugo Gerard & Kristoffer Nimark, 2008. "Combining multivariate density forecasts using predictive criteria," Economics Working Papers 1117, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2008.
    2. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.

    More about this item

    Keywords

    forecasting; factor models; Australia;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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