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Real-time forecasting in a data-rich environment

Listed author(s):
  • Liebermann, Joelle

    (Central Bank of Ireland)

This paper assesses the ability of different models to forecast key real and nominal U.S.monthly macroeconomic variables in a data-rich environment from the perspective of a realtime forecaster, i.e. taking into account the real-time data revisions process and data fl ow. We find that for the real variables predictability is confined over the recent recession/crisis period. This is in line with the findings of D'Agostino and Giannone (2012) that gains in relative performance of models using large datasets over univariate models are driven by downturn periods which are characterized by higher comovements. Regarding in flation, results are stable across time, but predictability is mainly found at the very short-term horizons. In flation is known to be hard to forecast, but by exploiting timely information one obtains gains at nowcasting and forecasting one-month ahead, especially with Bayesian VARs. Furthermore, for both real and nominal variables, the direct pooling of information using a high dimensional model (dynamic factor model or Bayesian VAR) which takes into account the cross-correlation between the variables and efficiently deals with the "ragged edge" structure of the dataset, yields more accurate forecasts than the indirect pooling of bi-variate forecasts/models.

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File URL: http://www.centralbank.ie/publications/Documents/07RT12.pdf
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Paper provided by Central Bank of Ireland in its series Research Technical Papers with number 07/RT/12.

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Date of creation: Dec 2012
Handle: RePEc:cbi:wpaper:07/rt/12
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