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Forecast content and content horizons for some important macroeconomic time series

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  • John W. Galbraith
  • Greg Tkacz

Abstract

. The information content of statistical forecasts of approximately stationary quantities tends to decline as the forecast horizon increases, and there exists a maximum horizon beyond which forecasts cannot provide discernibly more information about the variable than is present in the unconditional mean (the content horizon). The pattern of decay of forecast content (or skill) with increasing horizon is well known for many types of meteorological forecasts; by contrast, little generally accepted information about these patterns or content horizons is available for economic variables. In this paper we estimate content horizons for a variety of macroeconomic quantities; more generally, we characterize the pattern of decay of forecast content as we project farther into the future. We find a wide variety of results for the different macroeconomic quantities, with models for some quantities providing useful content several years into the future, for other quantities providing negligible content beyond one or two months or quarters. Le contenu informationnel des prévisions statistiques de données approximativement stationnaires tend à décliner à proportion que l'horizon temporel s'accroît, et il existe un horizon temporel maximal au‐delà duquel les prévisions ne peuvent pas fournir plus d'information discernable à propos de la variable que ce qui est contenu dans la moyenne inconditionnelle. La dégénération du contenu informationnel à mesure que l'horizon temporel s'accroît est bien connu pour plusieurs types de prévisions météorologiques; a contrario, on en connaît peu sur ces patterns pour les variables économiques. Dans ce texte, on calibre cet horizon temporel butoir pour une variété de variables macro‐économiques; plus généralement, on caractérise le profil de dégénération du contenu informationnel à mesure qu'on va de plus en plus loin dans l'avenir. On découvre une grande variété de résultats pour les diverses variables macro‐économiques : pour certaines données des modèles fournissent un contenu informationnel utile pour plusieurs années dans l'avenir alors que pour d'autres variables, le contenu informationnel devient négligeable au delà d'un ou deux mois ou trimestres.

Suggested Citation

  • John W. Galbraith & Greg Tkacz, 2007. "Forecast content and content horizons for some important macroeconomic time series," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(3), pages 935-953, August.
    • Handle: RePEc:wly:canjec:v:40:y:2007:i:3:p:935-953
    DOI: 10.1111/j.1365-2966.2007.00437.x
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    Cited by:

    1. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    2. John W. Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.
    3. John W. Galbraith & Simon van Norden, 2008. "The Calibration of Probabilistic Economic Forecasts," CIRANO Working Papers 2008s-28, CIRANO.
    4. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2023. "The accuracy and informativeness of agricultural baselines," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1116-1148, August.
    5. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    6. de Bruijn, L.P. & Franses, Ph.H.B.F., 2011. "Evaluating the Rationality of Managers' Sales Forecasts," Econometric Institute Research Papers EI 2011-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
    8. Baggio, Rodolfo, 2015. "Looking into the future of complex dynamic systems," MPRA Paper 65549, University Library of Munich, Germany.
    9. Jörg Breitung & Malte Knüppel, 2021. "How far can we forecast? Statistical tests of the predictive content," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 369-392, June.
    10. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    11. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
    12. François-Éric Racicota & David Tessierc, 2023. "On the relationship between Jorda?s IRF local projection and Dufour et al.?s robust (p,h)-autoregression multihorizon causality: a note," Working Papers 2023-001, Department of Research, Ipag Business School.
    13. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    14. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.

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    JEL classification:

    • 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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