Princeton University Library Catalog

The Use of Models in Producing OECD Macroeconomic Forecasts [electronic resource] / David Turner

Author:
Turner, David [Browse]
Format:
Book
Language:
English
Published/​Created:
Paris : OECD Publishing, 2016.
Description:
1 online resource
Series:
  • OECD Economics Department Working Papers, no.1336. [More in this series]
  • OECD Economics Department Working Papers, 1815-1973 ; no.1336
Summary note:
This paper firstly describes the role of models in producing OECD global macroeconomic forecasts; secondly, reviews the OECD's forecasting track record; and finally, considers the relationship between forecast performance and models. OECD forecasts are not directly generated from a single global model, but instead rely heavily on expert judgment which is informed by inputs from a range of different models, with forecasts subjected to repeated peer review. For the major OECD economies, current year GDP growth forecasts exhibit a number of desirable properties including that they are unbiased, outperform naïve forecasts and mostly identify turning points. Moreover, there is a trend improvement in current-year forecasting performance which is partly attributed to the increasing use of high frequency ‘now-casting' indicator models to forecast the current and next quarter's GDP. Conversely, the track record of one-year-ahead forecasts is much less impressive; such forecasts are biased, often little better than naïve forecasts and are poor at anticipating downturns. Forecasts tend to cluster around those from other international organisations and consensus forecasts; it is particularly striking that differences in one-year-ahead forecasts between forecasters are relatively minor in comparison with the size of average errors made by all of them. This may reflect herding behaviour by forecasters as well as the mean reversion properties of models. These weaknesses in forecasting performance beyond the current year underline the importance of increased efforts to use models to characterise the risk distribution around the baseline forecast, including through the increased use of model-based scenario analysis.
Doi:
  • 10.1787/5jlnb59tmdls-en
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