Forecasting Turkish real GDP growth in a data-rich environment

Bahar Şen Doğan () and Murat Midiliç ()
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Bahar Şen Doğan: Middle East Technical University
Murat Midiliç: Ghent University

Empirical Economics, 2019, vol. 56, issue 1, No 15, 367-395

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C22 C53 G10 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00181-017-1357-8

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