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Forecast Uncertainties In Macroeconometric Modelling: An Application to the UK Economy

Author

Listed:
  • Garratt, Anthony

    (University of Cambridge)

  • Kevin Lee

    (University of Leicester)

  • M Hashem Pesaran

    (Trinity College, Cambridge)

  • Yongcheol Shin

    (University of Edinburgh)

Abstract

This paper argues that probability forecasts convey information on the uncertainties that surround macro-economic forecasts in a straightforward manner which is preferable to other alternatives, including the use of confidence intervals. Point and probability forecasts obtained using a small macro-econometric model, are presented and evaluated using recursive forecasts generated from the model over the period 1999q1-2000q1. Out of sample probability forecasts of inflation and output growth are also provided over the period 2001q2-2003q1, and their implications discussed in relation to the Bank of England's inflation target and the need to avoid recessions, both as separate events and jointly. It is also shown how the probability forecasts can be used to provide insights on the inter-relationship of output growth and inflation at different horizons.

Suggested Citation

  • Garratt, Anthony & Kevin Lee & M Hashem Pesaran & Yongcheol Shin, 2002. "Forecast Uncertainties In Macroeconometric Modelling: An Application to the UK Economy," Royal Economic Society Annual Conference 2002 82, Royal Economic Society.
  • Handle: RePEc:ecj:ac2002:82
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    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Shin, Yongcheol & Smith, Richard J., 2000. "Structural analysis of vector error correction models with exogenous I(1) variables," Journal of Econometrics, Elsevier, vol. 97(2), pages 293-343, August.
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    6. Anthony Garratt & Kevin Lee & M. Hashem Pesaran & Yongcheol Shin, 2003. "A Long run structural macroeconometric model of the UK," Economic Journal, Royal Economic Society, vol. 113(487), pages 412-455, April.
    7. A. Robert Nobay & David A. Peel, 1998. "Optimal Monetary Policy in a Model of Asymmetric Central Bank Preferences," FMG Discussion Papers dp306, Financial Markets Group.
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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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