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Forecasting Turkish GDP Growth with Financial Variables and Confidence Indicators

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  • Mahmut Gunay

Abstract

[EN] This note evaluates the forecast performance of the financial variables and confidence indicators for four quarter ahead cumulative growth of Turkish GDP. Our results point out that some indicators can help reduce forecast errors relative to a benchmark, but forecast performance of the variables may change over time. Combining forecasts with equal weight or based on the recent performance does not lead to a significant difference in forecast performance. [TR] Bu calismada Turkiye ekonomisi icin finansal degiskenler ile guven endekslerinin dort ceyrek birikimli GSYIH buyumesi tahmin performanslari degerlendirilmistir. Sonuclar, incelenen degiskenlerin bazilarinin baz bir modele gore tahmin hatalarini dusurdugunu ancak tahmin performansinin zaman icinde degisebildigini gostermektedir. Tahmin birlestirmesi icin tahminlerin esit agirliklandirilmasi ile son donem performanslarina gore agirliklandirilmasi arasinda onemli bir fark gorulmemistir.

Suggested Citation

  • Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth with Financial Variables and Confidence Indicators," CBT Research Notes in Economics 1614, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:econot:1614
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    References listed on IDEAS

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    1. Stephane Dees & Pedro Soares Brinca, 2013. "Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area," International Economics, CEPII research center, issue 134, pages 1-14.
    2. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    3. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    4. Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 39-46, January.
    5. repec:cii:cepiei:2013-q2-134-1 is not listed on IDEAS
    6. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    7. Hatice Gökçe Karasoy Can & Çağlar Yüncüler, 2018. "The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(9), pages 2136-2152, July.
    8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    9. Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, June.
    10. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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