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The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time

Author

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  • A. Girardi
  • R. Golinelli
  • C. Pappalardo

Abstract

Building on the literature on regularization and dimension reduction methods, we have developed a quarterly forecasting model for euro area GDP. This method consists in bridging quarterly national accounts data using factors extracted from a large panel of monthly and quarterly series including business surveys and financial indicators. The pseudo real-time nature of the information set is accounted for as the pattern of publication lags is considered. Forecast evaluation exercises show that predictions obtained through various dimension reduction methods outperform both the benchmark AR and the diffusion index model without pre-selected indicators. Moreover, forecast combination significantly reduces forecast error.

Suggested Citation

  • A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    • Handle: RePEc:bol:bodewp:wp919
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    Cited by:

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    2. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    3. Mr. Andrew J Tiffin, 2016. "Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon," IMF Working Papers 2016/056, International Monetary Fund.
    4. Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.
    5. Christiana Anaxagorou & Nicoletta Pashourtidou, 2022. "Forecasting economic activity using preselected predictors: the case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 16(1), pages 11-36, June.
    6. Christos Papamichael & Nicoletta Pashourtidou, 2016. "The Role of Survey Data in the Construction of Short-term GDP Growth Forecasts," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 10(2), pages 77-109, December.
    7. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    8. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    9. 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.
    10. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    11. Tingguo Zheng & Xinyue Fan & Wei Jin & Kuangnan Fang, 2024. "Forecasting CPI with multisource data: The value of media and internet information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 702-753, April.
    12. Yutaka Kurihara & Akio Fukushima, 2019. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries," Applied Economics and Finance, Redfame publishing, vol. 6(3), pages 1-6, May.
    13. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    14. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    15. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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