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

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## Abstract

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.

## Keywords

*J*Demetra

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

## JEL Classification

C32 C55 C53 C87## References

- 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 - 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 - 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 - 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 - Bańbura, M., Giannone, D., & Reichlin, L. (2011). Nowcasting. In M. P. Clements, & D. F. Hendy (Eds.)
*Oxford handbook on economic forecasting*.Google Scholar - 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 - 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 - Bragoli, D. (2017). Now-casting the Japanese economy.
*International Journal of Forecasting*,*33*, 390–402.CrossRefGoogle Scholar - 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 - 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 - Camacho, M., & Pérez-Quirós, G. (2010). Introducing the euro-sting.
*Journal of Applied Econometrics*,*25*, 663–694.CrossRefGoogle Scholar - Claveria, O., Pons, E., & Ramos, R. (2007). Business and consumer expectations and macroeconomic forecasts.
*International Journal of Forecasting*,*23*, 47–69.CrossRefGoogle Scholar - 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 - Croushore, D., & Stark, T. (2002). Forecasting with a real-time data set for macroeconomists.
*Journal of Macroeconomics*,*24*, 507–531.CrossRefGoogle Scholar - 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 - de Antonio Liedo, D. (2015). Nowcasting Belgium.
*Eurostat review on National Accounts and Macroeconomic Indicators*,*75*, 7–48.Google Scholar - 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 - Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy.
*Journal of Business and Economic Statistics*,*13*, 253–265.Google Scholar - 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 - 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 - Durbin, J., & Koopman, S. J. (2001).
*Time series analysis by state space methods*. Oxford: Oxford University Press.Google Scholar - European Central Bank. (2011). The measurement and prediction of the euro area business cycle.
*Monthly Bulletin*, May 2011.Google Scholar - 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 - 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 - 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 - 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 - 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 - 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 - Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors.
*International Journal of Forecasting*,*13*, 281–291.CrossRefGoogle Scholar - Kalman, R. E. (1960). A new approach to linear filtering and prediction problems.
*Journal of Basic Engineering*,*82*, 35–45.CrossRefGoogle Scholar - 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 - Kiefer, N. M., & Vogelsang, T. J. (2005). A new asymptotic theory for heteroskedasticity-autocorrelation robust tests.
*Econometric Theory*,*21*(6), 1130–1164.CrossRefGoogle Scholar - 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 - 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 - 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 - Martinsen, K., Ravazzolo, F., & Wulfsberg, F. (2014). Forecasting macroeconomic variables using disaggregate survey data.
*International Journal of Forecasting*,*30*, 65–77.CrossRefGoogle Scholar - Modugno, M., Soybilgen, B., & Yazgan, E. (2016). Nowcasting Turkish GDP and news decomposition.
*International Journal of Forecasting*,*32*, 1369–1384.CrossRefGoogle Scholar - 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 - 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 - 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 - Rünstler, G. (2016). On the design of datasets for forecasting with dynamic factor models. In
*ECB Working Paper Series*,*1893*.Google Scholar - 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 - The Wall Street Journal. (1999). Euroland discovers a surprise indicator: Belgian confidence, 14 July.Google Scholar
- Williamson, C. (2015). Using PMI survey data to predict official eurozone GDP growth rates.
*Markit Economic Research*.Google Scholar