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A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP

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  • Bańbura, Marta
  • Rünstler, Gerhard

Abstract

We derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators. The use of the Kalman smoother allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information for the GDP forecasts beyond the monthly real activity measures. However, this is discovered only if their more timely publication is taken into account properly. Differences in publication lags play a very important role and should be considered in forecast evaluation.

Suggested Citation

  • Bańbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:333-346
    DOI: 10.1016/j.ijforecast.2010.01.011
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    More about this item

    Keywords

    Dynamic factor models; Filter weights; GDP; Publication lags;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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