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Combining forecasts from successive data vintages: An application to U.S. growth

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  • Götz, Thomas B.
  • Hecq, Alain
  • Urbain, Jean-Pierre

Abstract

We extend the repeated observations forecasting analysis of Stark and Croushore (2002) to allow for regressors that may be of higher sampling frequencies than the regressand. For the U.S. GNP quarterly growth rate, we compare the forecasting performances of an autoregressive model with those of several mixed-frequency models, including the MIDAS approach. Using the additional dimension provided by different vintages, we compute several forecasts for a given calendar date with each model, then approximate the corresponding distribution of forecasts by a continuous density. Next, we combine these model-specific densities using scoring rules and analyze both the composition and the evolution of the implied weights over time. In so doing, not only do we investigate the sensitivity of model selection to the choice of which data release to consider, we also illustrate how revision process information can be incorporated into real time studies. As a consequence of these analyses, we introduce a new weighting scheme that summarizes the information contained in the revision process of the variables under consideration.

Suggested Citation

  • Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
  • Handle: RePEc:eee:intfor:v:32:y:2016:i:1:p:61-74
    DOI: 10.1016/j.ijforecast.2015.04.003
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    Cited by:

    1. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    2. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    3. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

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