Nowcasting Real GDP Growth: Comparison between Old and New EU Countries Evžen Kočenda a,b,c,d and Karen Poghosyan e a Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic; b Institute of Theory of Information and Automation, Czech Academy of Sciences, Prague, Czech Republic; c CESifo, Munich, Germany; d IOS, Regensburg, Germany; e Economic Research Department, Central Bank of Armenia, Yerevan, Armenia ABSTRACT We analyze the performance of a broad range of nowcasting and short-term forecasting models for a representative set of twelve old and six new member countries of the European Union (EU) that are characterized by substantial differences in aggregate output variabil- ity. In our analysis, we generate ex-post out-of-sample nowcasts and forecasts based on hard and soft indicators that come from a comparable set of identical data. We show that nowcasting works well for the new EU countries because, although that variability in their GDP growth data is larger than that of the old EU economies, the economic significance of nowcasting is on average somewhat larger. KEYWORDS Bayesian VAR; dynamic and static principal components; european OECD countries; factor augmented VAR; nowcasting; real GDP growth; short-term forecasting JEL CLASSIFICATION C33; C38; C52; C53; E37; E52 Introduction Effective economic policy in any country is conditioned by the availability of timely and accurate economic data and forecasts of them (Banbura et al. 2013; Giannone, Reichlin, and Small 2008; Jansen, Jin, and de Winter 2016). A typical case is represented by central banks, in which policy makers, as a rule, have to make decisions in real time with incomplete informa- tion on current economic conditions. The issue is even more important in emerging econo- mies, where variation in economic activity is often high, and data availability might be less than perfect (Bragoli and Fosten 2018; Bragoli, Metelli, and Modugno 2015; Giannone, Agrippino, and Modugno 2013; Luciani et al. 2018). Typically, the data are not available in the required time or are incomplete, and the resulting accuracy of forecasts might be plagued by volatility in the input data. We explore the issue of forecast accuracy with a set of old and new member countries of the European Union (EU) for which a comparable set of identical data is available. Specifically, we compare the forecast accuracy of nowcasting 1 and forecasting algorithms based on the use of data on the real economy from eighteen European countries characterized by different output volatility regimes. 2 Our goal is to show which algorithm delivers the most accurate short-term forecasts of the growth in the real gross domestic product (GDP) and how the results differ between old and new EU members. Our analysis specifically assesses GDP growth forecasts because GDP is one of the most comprehensive macroeconomic indicators of economic activity. Thus, GDP growth is the target for providing important information in the policy-making process. However, for CONTACT Evžen Kočenda evzen.kocenda@fsv.cuni.cz Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, Prague 110 00, Czech Republic EASTERN EUROPEAN ECONOMICS 2020, VOL. 58, NO. 3, 197–220 https://doi.org/10.1080/00128775.2020.1726185 © 2020 Taylor & Francis Group, LLC