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Nowcasting Real GDP Growth: Comparison between Old and New EU Countries

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  • Evzen Kocenda

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Czech Republic. Address: IES, Opletalova 26, 110 00 Prague.
    Institute of Theory of Information and Automation, Czech Academy of Sciences, Prague; CESifo Munich; IOS Regensburg.)

  • Karen Poghosyan

    (Central Bank of Armenia, Economic Research Department, V. Sargsyan 6, 0010, 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 variability. 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.

Suggested Citation

  • Evzen Kocenda & Karen Poghosyan, 2020. "Nowcasting Real GDP Growth: Comparison between Old and New EU Countries," Working Papers IES 2020/5, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2020.
    • Handle: RePEc:fau:wpaper:wp2020_05
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    File URL: https://ies.fsv.cuni.cz/en/veda-vyzkum/working-papers/6217
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    Cited by:

    1. Angelos Kanas & Panagiotis D. Zervopoulos, 2021. "Systemic risk, real GDP growth, and sentiment," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 461-485, August.
    2. Poghosyan, Karen & Poghosyan, Ruben, 2021. "On the applicability of dynamic factor models for forecasting real GDP growth in Armenia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 28-46.

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    More about this item

    Keywords

    Bayesian VAR; dynamic and static principal components; European OECD countries; factor augmented VAR; nowcasting; real GDP growth; short-term forecasting;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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