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Forecasting Turkish Real GDP Growth in a Data Rich Environment


  • Bahar Sen Dogan
  • Murat Midilic


This study generates nowcasts and forecasts for the growth rate of the Gross Domestic Product (GDP) in Turkey using 204 daily financial series with Mixed Data Sampling (MIDAS) framework over the period 2010Q2-2015Q1. Our findings 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 our 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.

Suggested Citation

  • Bahar Sen Dogan & Murat Midilic, 2016. "Forecasting Turkish Real GDP Growth in a Data Rich Environment," Working Papers 1611, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
  • Handle: RePEc:tcb:wpaper:1611

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    Real GDP Growth; Forecasting; MIDAS;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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