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Nowcasting Unemployment Rate in Turkey : Let's Ask Google


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  • Meltem Gulenay Chadwick
  • Gonul Sengul


We use linear regression models and Bayesian Model Averaging procedure to investigate whether Google search query data can improve the nowcast performance of the monthly nonagricultural unemployment rate for Turkey for the period from January 2005 to January 2012. We show that Google search query data is successful at nowcasting1 monthly nonagricultural unemployment rate for Turkey both in-sample and out-of-sample. When compared with a benchmark model, where we use only the lag values of the monthly unemployment rate, the best model contains Google search query data and it is 47.8 percent more accurate in-sample and 38.3 percent more accurate for the one month ahead nowcasts in terms of relative root mean square errors (RMSE). We also show via Harvey, Leybourne, and Newbold (1997) modification of the Diebold-Mariano test that models with Google search query data indeed perform statistically better than the benchmark.

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Bibliographic Info

Paper provided by Research and Monetary Policy Department, Central Bank of the Republic of Turkey in its series Working Papers with number 1218.

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Date of creation: 2012
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Handle: RePEc:tcb:wpaper:1218

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Keywords: Google Insights; nowcasting; nonagricultural unemployment rate; Bayesian model averaging;

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Cited by:
  1. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.


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