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Using Newspapers for Tracking the Business Cycle: A comparative study for Germany and Switzerland

On the basis of keyword searches in newspaper articles several versions of the Recession-word Index (RWI) are constructed for Germany and Switzerland. We use these indices in order to track the business cycle dynamics in these two countries. Our main findings are the following. First, we show that augmenting benchmark autoregressive models with the RWI generally leads to improvement in accuracy of one-step ahead forecasts of GDP growth compared to those obtained by the benchmark model. Second, the accuracy of out-of-sample forecasts obtained with models augmented with the RWI is comparable to that of models augmented with established economic indicators in both countries, such as the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment for Germany, and the KOF Economic Barometer and the Purchasing Managers Index in manufacturing for Switzerland. Third, we show that the RWI-based forecasts are more accurate than the consensus forecasts (published by Consensus Economics Inc.) for Switzerland, whereas we reach the opposite conclusion for Germany. In fact, the accuracy of the consensus forecasts of GDP growth for Germany appears to be superior to that of any other indicator considered in our study. These results are robust to changes in estimation/forecast samples, the use of rolling vs expanding estimation windows, and the inclusion of a web-based recession indicator extracted from Google Trends into a set of the competing models.

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Paper provided by KOF Swiss Economic Institute, ETH Zurich in its series KOF Working papers with number 13-337.

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Length: 36 pages
Date of creation: Jun 2013
Date of revision:
Handle: RePEc:kof:wpskof:13-337
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  1. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
  2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  3. Jan Grossarth-Maticek & Johannes Mayr, 2008. "Medienberichte als Konjunkturindikator," Ifo Schnelldienst, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(07), pages 17-29, 04.
  4. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research.
  5. 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.
  6. Matthias W. Uhl, 2011. "Nowcasting Private Consumption with TV Sentiment," KOF Working papers 11-293, KOF Swiss Economic Institute, ETH Zurich.
  7. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  8. Harm Bandholz & Michael Funke, 2001. "In Search of Leading Indicators of Economic Activity in Germany," CESifo Working Paper Series 571, CESifo Group Munich.
  9. Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2002. "The use and abuse of 'real-time' data in economic forecasting," Working Papers 2001-015, Federal Reserve Bank of St. Louis.
  10. Boriss Siliverstovs & Konstantin Kholodilin, 2012. "Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany," ERSA conference papers ersa12p911, European Regional Science Association.
  11. Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Research Notes of the German Council for Social and Economic Data 41, German Council for Social and Economic Data (RatSWD).
  12. Mark Doms & Norman J. Morin, 2004. "Consumer sentiment, the economy, and the news media," Working Paper Series 2004-09, Federal Reserve Bank of San Francisco.
  13. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
  14. Ammann, Manuel & Frey, Roman & Verhofen, Michael, 2012. "Do Newspaper Articles Predict Aggregate Stock Returns?," Working Papers on Finance 1204, University of St. Gallen, School of Finance.
  15. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, 08.
  16. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  17. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
  18. Mark Doms, 2004. "Consumer sentiment and the media," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue oct22.
  19. D'Amuri, Francesco & Marcucci, Juri, 2009. "'Google it!' Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
  20. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, 06.
  21. David Iselin & Boriss Siliverstovs, 2012. "The R-word Index for Switzerland," KOF Working papers 12-304, KOF Swiss Economic Institute, ETH Zurich.
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