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Forecasting Some Low-Predictability Time Series Using Diffusion Indices

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  • Marc Brisson
  • Bryan Campbell
  • John Galbraith

Abstract

The growth rates of real output and real investment are two macroeconomic time series which are particularly difficult to forecast. This paper considers the application of diffusion index forecasting models to this problem. We begin by characterizing the performance of standard forecasts, via recently-introduced measures of predictability and the forecast content, noting the maximum horizon at which the forecasts have value. We then compare diffusion index forecasts with a variety of alternatives, including the forecasts made by the OECD. We find gains in forecast accuracy at short horizons from the diffusion index models, but do not find evidence that the maximum horizon for forecasts can be extended in this way. Les taux de croissance de production et d'investissements réels sont deux séries macroéconomiques qui sont particulièrement difficiles à prévoir. Nous considérons dans cet article l'application des méthodes d'indice de diffusion à ce problème. Nous commençons avec une caractérisation de la performance des méthodes de prévision standards, via les mesures nouvelles de prévisibilité et la valeur ajoutée des prévisions, en notant l'horizon maximal auquel les prévisions ont de la valeur. Nous comparons les prévisions provenant des indices de diffusion avec les alternatives, incluant les prévisions de l'OCDE. Nous trouvons des gains en précision des prévisions,0501s ne trouvons pas que l'horizon maximal de prévision peut être augmenté.

Suggested Citation

  • Marc Brisson & Bryan Campbell & John Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.
  • Handle: RePEc:cir:cirwor:2001s-46
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    File URL: http://www.cirano.qc.ca/files/publications/2001s-46.pdf
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    4. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    5. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
    6. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
    7. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
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    Citations

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    Cited by:

    1. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    2. Moon, H.R.Hyungsik Roger & Perron, Benoit, 2004. "Testing for a unit root in panels with dynamic factors," Journal of Econometrics, Elsevier, vol. 122(1), pages 81-126, September.
    3. Roberto Tatiwa Ferreira & Herman Bierens & Ivan Castelar, 2005. "Forecasting Quarterly Brazilian GDP Growth Rate With Linear and NonLinear Diffusion Index Models," Economia, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 6(3), pages 261-292.
    4. Boriss Siliverstovs & Kinstantin Kholodilim, 2009. "On selection of components for a diffusion index model: it's not the size, it's how you use it," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1249-1254.
    5. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Staff Working Papers 01-18, Bank of Canada.
    6. John W. Galbraith & Greg Tkacz, 2007. "Forecast content and content horizons for some important macroeconomic time series," Canadian Journal of Economics, Canadian Economics Association, vol. 40(3), pages 935-953, August.
    7. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
    8. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
    9. Bryan Campbell & Steve Murphy, 2006. "The Recent Performance of the Canadian Forecasting Industry," Canadian Public Policy, University of Toronto Press, vol. 32(1), pages 23-40, March.
    10. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    11. Schumacher, Christian, 2010. "Factor forecasting using international targeted predictors: The case of German GDP," Economics Letters, Elsevier, vol. 107(2), pages 95-98, May.
    12. John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.
    13. Marcel Dagenais & Claude Montmarquette & Nathalie Viennot-Briot, 2001. "Dropout, School Performance and Working while in School : An Econometric Model with Heterogeneous Groups," CIRANO Working Papers 2001s-63, CIRANO.
    14. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

    More about this item

    Keywords

    Diffusion index; forecasting; investment; GDP; Indice de diffusion; prévisions; investissement; PNB;

    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

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