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Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?

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  • Bec, Frédérique
  • Mogliani, Matteo

Abstract

This paper empirically investigates two alternative combination strategies, namely forecast combination and information pooling, in the context of nowcasting French GDP in real time with monthly survey opinions. According to the encompassing paradigm, we claim that the outperformance of the forecast combination strategy reported by recent works may be related to the issues of model selection and misspecification. To address these issues, we promote the blocking modeling approach to allow us to handle mixed frequencies in a linear framework that is compatible with an automatic model selection algorithm. Selected restricted- and pooled-information models are specified and tested for forecast encompassing in order to determine the best combination strategy. The results suggest that the forecast combination strategy dominates as long as no individual (restricted) model encompasses the rivals. However, when a predictive encompassing model is obtained by pooling the information sets, this model outperforms the most accurate forecast combination scheme.

Suggested Citation

  • Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:4:p:1021-1042
    DOI: 10.1016/j.ijforecast.2014.11.006
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    Cited by:

    1. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    2. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    3. Matteo Mogliani & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.
    4. Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
    5. Christian Gayer & Alessandro Girardi & Andreas Reuter, 2016. "Replacing Judgment by Statistics: Constructing Consumer Confidence Indicators on the basis of Data-driven Techniques. The Case of the Euro Area," Working Papers LuissLab 16125, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    6. E. Monnet & C. Thubin, 2017. "Construction crises and business cycle: consequences for GDP forecasts," Rue de la Banque, Banque de France, issue 39, february..

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    More about this item

    Keywords

    Forecast combination; Information pooling; GDP nowcasting; Model selection; Mixed-frequency data;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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