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Empirical Economics

, Volume 56, Issue 1, pp 367–395 | Cite as

Forecasting Turkish real GDP growth in a data-rich environment

  • Bahar Şen Doğan
  • Murat Midiliç
Article

Abstract

This study generates nowcasts and forecasts for the growth rate of the gross domestic product in Turkey using 204 daily financial series with mixed data sampling (MIDAS) framework. The daily financial series include commodity prices, equity indices, exchange rates, and global and domestic corporate risk series. Forecasting exercises are also carried out with the daily factors extracted from separate financial data classes and from the whole dataset. The findings of the study suggest that MIDAS regression models and forecast combinations provide advantage in exploiting information from daily financial data compared to the models using simple aggregation schemes. In addition, incorporating daily financial data into the analysis improves the forecasts substantially. These results indicate that both the information content of the financial data and the flexible data-driven weighting scheme of MIDAS regressions play an essential role in forecasting the future state of the Turkish economy.

Keywords

Real GDP growth Forecasting MIDAS 

JEL Classification

C22 C53 G10 

Notes

Acknowledgements

Authors thank the editor and two anonymous referees of the journal, Koen Inghelbrecht and Gert Peersman, for valuable comments and suggestions.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Economics DepartmentMiddle East Technical UniversityAnkaraTurkey
  2. 2.Department of Financial EconomicsGhent UniversityGhentBelgium

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