Journal of Business Cycle Research

, Volume 14, Issue 1, pp 1–46 | Cite as

Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys

  • Raïsa Basselier
  • David de Antonio Liedo
  • Geert Langenus
Research Paper


This paper analyses the contribution of survey data, in particular various sentiment indicators, to nowcasts of quarterly euro area GDP. It uses a genuine real-time dataset that is constructed from original press releases in order to transform the actual dataflow into an interpretable flow of news. The latter is defined as the difference between the released values and the prediction of a mixed-frequency dynamic factor model. Our purpose is twofold. First, we aim to quantify the specific value added for nowcasting GDP from a set of heterogeneous data releases including not only sentiment indicators constructed by Eurostat, Markit, the National Bank of Belgium, IFO, ZEW, GfK or Sentix, but also hard data regarding industrial production or retail sales in the aggregate euro area and individually in some of the largest euro area countries. Second, our quantitative analysis is used to draw up an overall ranking of the indicators, on the basis of their average contribution to updates of the nowcast. Among the survey indicators, we find the strongest impact for the Markit Manufacturing PMI and the Business Climate Indicator in the euro area, and the IFO Business Climate and IFO Expectations in Germany. The widely monitored consumer confidence indicators, on the other hand, typically do not lead to significant revisions of the nowcast. In addition, even if euro area industrial production is a relevant predictor, hard data generally contribute less to the nowcasts: they may be more closely correlated with GDP but their relatively late availability implies that they can to a large extent be anticipated by nowcasting on the basis of survey data and, hence, their ‘news’ component is smaller. Finally, we also show that, in line with the previous literature, the NBB’s own business confidence indicator appears to be useful for predicting euro area GDP. The prevalence of survey data remains also under a counterfactual scenario in which hard data are released without any delay. This finding confirms that, in addition to being available in a more timely manner, survey data also contain relevant information that does not seem to be captured by hard data.


JDemetra+Nowcasting Surveys News Dynamic factor models Press releases Real-time data Bloomberg Forex Factory Kalman gain 

JEL Classification

C32 C55 C53 C87 


  1. Abberger, K. (2007). Qualitative business surveys and the assessment of employment—A case study for Germany. International Journal of Forecasting, 23, 249–258.CrossRefGoogle Scholar
  2. Angelini, E., Camba-Mendez, G., Giannone, D., Rünstler, G., & Reichlin, L. (2011). Short-term forecasts of euro area GDP growth. The Econometrics Journal, 1, C25–C44.CrossRefGoogle Scholar
  3. Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business and Economic Statistics, American Statistical Association, 27(4), 417–427.CrossRefGoogle Scholar
  4. Bańbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Nowcasting and the real-time data flow. In G. Elliot & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 2). Amsterdam: Elsevier.Google Scholar
  5. Bańbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. In M. P. Clements, & D. F. Hendy (Eds.)Oxford handbook on economic forecasting.Google Scholar
  6. Bańbura, M., & Modugno M. (2010). Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data. In ECB working paper series, 1189 Google Scholar
  7. Bańbura, M., & Rünstler, G. (2011). A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP. International Journal of Forecasting, 27(2), 333–346.CrossRefGoogle Scholar
  8. Bragoli, D. (2017). Now-casting the Japanese economy. International Journal of Forecasting, 33, 390–402.CrossRefGoogle Scholar
  9. Bragoli, D., Metelli, L., & Modugno, M. (2015). The importance of updating: Evidence from a Brazilian nowcasting model. OECD Journal: Journal of Business Cycle Measurement and Analysis, 1, 5–22.Google Scholar
  10. Bragoli, D., & Modugno, M. (2017). A now-casting model for Canada: Do U.S. variables matter? International Journal of Forecasting, 33, 786–800.CrossRefGoogle Scholar
  11. Camacho, M., & Pérez-Quirós, G. (2010). Introducing the euro-sting. Journal of Applied Econometrics, 25, 663–694.CrossRefGoogle Scholar
  12. Claveria, O., Pons, E., & Ramos, R. (2007). Business and consumer expectations and macroeconomic forecasts. International Journal of Forecasting, 23, 47–69.CrossRefGoogle Scholar
  13. Coroneo, L., & Iacone, F. (2016). Comparing predictive accuracy in small samples using fixed-smoothing asymptotics. In Discussion papers University of York, 15/15.Google Scholar
  14. Croushore, D., & Stark, T. (2002). Forecasting with a real-time data set for macroeconomists. Journal of Macroeconomics, 24, 507–531.CrossRefGoogle Scholar
  15. D’Agostino, K. Mc Quinn, & O’Brien, D. (2013). Nowcasting Irish GDP. OECD Journal: Journal of Business Cycle Measurement and Analysis, 7, 21–31.Google Scholar
  16. de Antonio Liedo, D. (2015). Nowcasting Belgium. Eurostat review on National Accounts and Macroeconomic Indicators, 75, 7–48.Google Scholar
  17. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.Google Scholar
  18. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253–265.Google Scholar
  19. Diron, M. (2008). Short-term forecasts of euro area real GDP growth: An assessment of real-time performance based on vintage data. Journal of Forecasting, 27, 371–390.CrossRefGoogle Scholar
  20. Doz, C., Giannone, D., & Reichlin, L. (2012). A quasi maximum likelihood approach for large approximate dynamic factor models. Review of Economics and Statistics, 94, 1014–1024.CrossRefGoogle Scholar
  21. Durbin, J., & Koopman, S. J. (2001). Time series analysis by state space methods. Oxford: Oxford University Press.Google Scholar
  22. European Central Bank. (2011). The measurement and prediction of the euro area business cycle. Monthly Bulletin, May 2011.Google Scholar
  23. Evans, M. D. D. (2005). Where are we now? Real-time estimates of the macroeconomy. International Journal of Central Banking, 1, 127–175.Google Scholar
  24. Frale, C., Marcellino, M., Mazzi, G. L., & Proietti, T. (2011). EUROMIND: A monthly indicator of the euro area economic conditions. Journal of the Royal Statistical Society: Series A, 174, 439–470.CrossRefGoogle Scholar
  25. Franta, D., Havrlant, D., & Runsák, M. (2016). Forecasting Czech GDP using mixed-frequency data models. Journal of Business Cycle Research, 12, 165–185.CrossRefGoogle Scholar
  26. Gayer, A., Girardi, A., & Reuter, A. (2015). The role of survey data in nowcasting euro area GDP growth. Journal of Forecasting, 35, 400–418.Google Scholar
  27. Giannone, D., Reichlin, L., & Simonelli, S. (2009). Nowcasting euro area economic activity in real time: The role of confidence indicators. National Institute Economic Review, 210, 90–97.CrossRefGoogle Scholar
  28. Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data releases. Journal of Monetary Economics, 55, 665–676.CrossRefGoogle Scholar
  29. Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13, 281–291.CrossRefGoogle Scholar
  30. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45.CrossRefGoogle Scholar
  31. Koopman, S. J. & Harvey, A. (2003) Computing observation weights for signal extraction and filtering. Journal of Economic Dynamics and Control, 27, 1317–1333.Google Scholar
  32. Kiefer, N. M., & Vogelsang, T. J. (2005). A new asymptotic theory for heteroskedasticity-autocorrelation robust tests. Econometric Theory, 21(6), 1130–1164.CrossRefGoogle Scholar
  33. Kishor, N. K., & Koenig, E. F. (2012). VAR estimation and forecasting when data are subject to revision. Journal of Business and Economic Statistics, 30, 182–190.Google Scholar
  34. Lui, S., Mitchell, J., & Weale, M. (2011). Qualitative business surveys: Signal or noise? Journal of the Royal Statistical Society: Series A, 174, 327–348.CrossRefGoogle Scholar
  35. Mariano, R. S., & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics, 18, 427–443.CrossRefGoogle Scholar
  36. Martinsen, K., Ravazzolo, F., & Wulfsberg, F. (2014). Forecasting macroeconomic variables using disaggregate survey data. International Journal of Forecasting, 30, 65–77.CrossRefGoogle Scholar
  37. Modugno, M., Soybilgen, B., & Yazgan, E. (2016). Nowcasting Turkish GDP and news decomposition. International Journal of Forecasting, 32, 1369–1384.CrossRefGoogle Scholar
  38. Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.CrossRefGoogle Scholar
  39. Patton, A. J., & Timmerman, A. (2012). Forecast rationality tests based on multi-horizon bounds. Journal of Business and Economic Statistics, 30(1), 1–17.CrossRefGoogle Scholar
  40. Piette, C., & Langenus, G. (2014). Using BREL to now-cast the Belgian business cycle: The role of survey data. Economic Review. National Bank of Belgium, pp. 75–98.Google Scholar
  41. Rünstler, G. (2016). On the design of datasets for forecasting with dynamic factor models. In ECB Working Paper Series, 1893.Google Scholar
  42. Shumway, R., & Stoffer, D. (1982). An approach to time series smoothing and forecasting using the EM algorithm. Journal of Time Series Analysis, 3, 253–264.CrossRefGoogle Scholar
  43. The Wall Street Journal. (1999). Euroland discovers a surprise indicator: Belgian confidence, 14 July.Google Scholar
  44. Williamson, C. (2015). Using PMI survey data to predict official eurozone GDP growth rates. Markit Economic Research.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Economics and Research DepartmentNational Bank of BelgiumBrusselsBelgium
  2. 2.R&D StatisticsNational Bank of BelgiumBrusselsBelgium

Personalised recommendations