This study compares industrial production and gross value added in volume terms in the euro area and euro area countries, because real GDP growth signals from industrial production growth might be misleading and earlier released industrial production growth is not one-to-one translated into industrial value added growth. This is an important issue for analysts and policy makers, because industrial production is a standard element in tools for nowcasting real GDP in real time. It also raises the question about the factors explaining these differences. Differences in terms of (changes in) quarterly growth between production and gross value added include sign reversals and can last for consecutive quarters. Persistent level differences might also exist. The explanatory factors for these differences are the treatment of prices, seasonality and coverage. Data limitations prevent a detailed analysis of the price factor, but the other two factors are more closely evaluated. It turns out that the relative importance of these factors varies over time and thus is difficult to assess ex ante for a specific quarter. A remedy is that statisticians further harmonize national accounts and short-term statistics as well as national practices for seasonal adjustment.
Andreoni, A., S. Upadhyaya (2014), Growth and Distribution Pattern of the World Manufacturing Output: A Statistical Profile. United Nations Industrial Development Organization Working Paper 02/2014.
Banbura, M., D. Giannone, M. Modugno, L. Reichlin 2013, Now-Casting and the Real-Time Data Flow. In G. Elliott, A. Timmermann (Hrsg.),Handbook of Economic Forecasting. Vol. 2. ch. 4. Amsterdam: Elsevier.
Bengoechea, P., M. Camacho, G. Perez-Quiros 2006, A Useful Tool for Forecasting the Euro-Area Business Cycle Phases. International Journal of Forecasting 22: 735–749.
Bengoechea, P., M. Camacho, G. Perez-Quiros 2006, A Useful Tool for Forecasting the Euro-Area Business Cycle Phases. International Journal of Forecasting 22: 735–749.10.1016/j.ijforecast.2006.01.002)| false
Camba-Mendez, G., G. Kapetanios, M.R. Weale, F. Papailias 2017, An Automatic Leading Indicator, Variable Reduction and Variable Selection Methods Using Small and Large Datasets: Forecasting the Industrial Production Growth for Euro Area Economies. in G.L. Mazzi, A. Ozyildirim (Hrsg.), Handbook on Cyclical Composite Indicators for Business Cycle Analysis. ch. 16. Luxembourg: United Nations and Eurostat.
Carstensen, K., K. Wohlrabe, C. Ziegler 2011. Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production. Journal of Economics and Statistics 231 (1): 82–106.
De Bondt, G.J. (2012), Nowcasting: Trust the Purchasing Managers’ Index or Wait for the Flash GDP Estimate?. Conference Paper, presented at EcoMod2012, International Conference on Economic Modeling, July 4-6. Seville.
De Bondt, G.J., E. Hahn (2010), Predicting Recessions and Recoveries in Real Time: The Euro Area-Wide Leading Indicator (ALI), ECB Working Paper 1246.
De Bondt, G.J., E. Hahn 2014, Introducing the Euro Area-Wide Leading Indicator (ALI): Real-Time Signals of Turning Points from 2007-2011. Journal of Forecasting 33 (1): 47–68.
Durand, R. 1994, Alternative Estimates of Real Value Added by Industry for Canada. Economic Systems Research 8: 225–234.
European Central Bank (2004), Differences between Industrial Production and Value Added Data in Industry in the First Quarter of 2004, Monthly Bulletin, August: 40–42.
European Central Bank 2016, A Closer Look at Differences between Industrial Gross Value Added and Industrial Production. Economic Bulletin 1: 19–22.
European Commission (2017), ESI and Other BCS Indicators Vs PMI – Properties and Empirical Performance, Special Topic In: European Business Cycle Indicators 2nd Quarter 2017, European Economy Technical Paper 017, July, 18-26.Eurostat (2003), Methodology of short-term business statistics.
Kuzin, V., M. Massimiliano, C. Schumacher 2011, MIDAS Vs. Mixed-Frequency VAR: Nowcasting GDP in the Euro Area. International Journal of Forecasting 27 (2): 529–542.10.1016/j.ijforecast.2010.02.006)| false
Lucke, D., J.-P. Weiß (2002), International Comparison of Industrial Development in the European Context – The Problems. Economic Bulletin 39. German Institute for Economic Research: 215–220.
Mazzi, G.L., J. Mitchell, G. Montana 2014, Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession. Oxford Bulletin of Economics and Statistics 76 (2): 233–256.
Mazzi, G.L., J. Mitchell, G. Montana 2014, Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession. Oxford Bulletin of Economics and Statistics 76 (2): 233–256.10.1111/obes.12015)| false
Vanhaelen, -J.-J., L. Dresse, J. De Mulder (2000), The Belgian Industrial Confidence Indicator: Leading Indicator of Economic Activity in the Euro Area? National Bank of Belgium Working Paper 12.
Waldmann, R.J. 1991, Implausible Results or Implausible Data? Anomalies in the Construction of Value-Added Data and Implications for Estimates of Price-Cost Markups. Journal of Political Economy 99: 1315–1328.
Waldmann, R.J. 1991, Implausible Results or Implausible Data? Anomalies in the Construction of Value-Added Data and Implications for Estimates of Price-Cost Markups. Journal of Political Economy 99: 1315–1328.10.1086/261802)| false
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