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Combining distributions of real-time forecasts: An application to U.S. growth

Author

Listed:
  • Götz T.B.
  • Hecq A.W.
  • Urbain J.R.Y.J.

    (GSBE)

Abstract

We extend the repeated observations forecasting ROF analysis of Croushore and Stark 2002 to allow for regressors of possibly higher sampling frequencies than the regressand. For the U.S. GNP quarterly growth rate, we compare the forecasting performances of an AR model with several mixed-frequency models among which is the MIDAS approach. Using the additional dimension provided by different vintages we compute several forecasts for a given calendar date and subsequently approximate the corresponding distribution of forecasts by a continuous density. Scoring rules are then employed to construct combinations of them and analyze the composition and evolvement of the implied weights over time. Using this approach, we not only investigate the sensitivity of model selection to the choice of which data release to consider, but also illustrate how to incorporate revision process information into real-time studies. As a consequence of these analyses, weintroduce a new weighting scheme that summarizes information contained in the revision process of the variables under consideration.

Suggested Citation

  • Götz T.B. & Hecq A.W. & Urbain J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
  • Handle: RePEc:unm:umagsb:2014027
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    Cited by:

    1. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    2. Hecq A.W. & Urbain J.R.Y.J. & Götz T.B., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).

    More about this item

    Keywords

    Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series; Forecasting and Prediction Methods; Simulation Methods ;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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