Economic sentiment indicators and their prediction capabilities in business cycles of EU countries

Authors

DOI:

https://doi.org/10.24136/oc.2023.029

Keywords:

business cycle, cross correlation, prediction, ESI, GDP, IIP

Abstract

Research background: The post-World Financial Crisis period has showed us that an application of the qualitative data focused on the expectations of the enterprises and consumers in a combination with the quantitative data in the individual economy sectors is a good prerequisite for reliable prediction of the economic cycles.

Purpose of the paper: The main goal of the presented study was to test the ESI prediction capabilities and its components in a relation to the economic cycles of the EU countries in the individual time periods.

Methods: The time series for the period Q1 2000 to Q4 2022 and the three selected time periods were a subject to undergo the selection of the cyclical component applying the Hodrick-Prescott filter and then, the relationship between the variables was determined employing the Pearson correlation coefficient with the time shifts. The relation of ESI and its components to GDP and the Index of Industrial Production (IIP), which represent the economic cycle, was analysed. The prediction volume and the cross-correlation values determined the nature of the observed cyclical variables.

Findings & value added: The results of the analysis point to the fact that ESI and its components are able to ensure a high-quality prediction of the economic cycle only in the selected EU countries. Regarding the components of the ESI, the Consumer confidence indicator, Construction and Industrial confidence indicators show the best predictive capabilities. The analytical outcomes show that the ESI size and lead period vary over time and after the 2008 crisis, the ESI showed better predictive capabilities in a relation to GDP and IIP than before the crisis. The Covid 19 pandemic had a significant negative impact on the ESI predictive capabilities.

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References

Adamowicz, E., & Walczyk, K.. (2013). New EU countries after the great recession. Transformations in Business & Economics, 1(2B), 255–265.
View in Google Scholar

Aguilar, P., Ghirelli, C., Pacce, M., & Urtasun, A. (2021). Can news help measure economic sentiment? An application in COVID-19 times. Economics Letters, 199(C). 109730.

DOI: https://doi.org/10.1016/j.econlet.2021.109730
View in Google Scholar

Altin, M., & Uysal, M. (2014). Economic sentiment indicator as a demand determinant. Tourism Analysis, 19(5), 581–597.

DOI: https://doi.org/10.3727/108354214X14116690097855
View in Google Scholar

Androniceanu, A. (2020). Major structural changes in the EU policies due to the problems and risks caused by COVID-19. Administratie si Management Public, 34, 137–149.

DOI: https://doi.org/10.24818/amp/2020.34-08
View in Google Scholar

Antipa, P., Barhoumi, K., Brunhes-Lesage, V., & Darne, O. (2012). Nowcasting German GDP - A comparison of bridge and factor models. Journal of Policy Modeling, 34(6), 864–878.

DOI: https://doi.org/10.1016/j.jpolmod.2012.01.010
View in Google Scholar

Astolfi, R., Gamba, M., Guidetti, E., & Pionnier, P. A. (2016). The use of short-term indicators and survey data for predicting turning points in economic activity: A performance analysis of the OECD system of CLIs during the Great Recession. OECD Statistics Working Papers, 8.
View in Google Scholar

Badea, L., Panait, I., Socol, A., & Moraru, A. D. (2018). Sentiment, perception and policy determinants of foreign direct investment to European developing countries. Economic Computation & Economic Cybernetics Studies & Research, 52(2), 69–85.

DOI: https://doi.org/10.24818/18423264/52.2.18.05
View in Google Scholar

Biau, O., & D’Elia, A. (2011). Is there a decoupling between soft and hard data? The relationship between GDP growth and the ESI. In Fifth joint EU-OECD workshop on international developments in business and consumer tendency surveys (pp. 1–7). Brussels: European Commission.
View in Google Scholar

Bonadio, B., Huo, Zh., Levchenko, A., & Pandalai-Nayar, N. (2020). Global supply chains in the pandemic. Journal of International Economics, 133, 1–23,.

DOI: https://doi.org/10.3386/w27224
View in Google Scholar

Brzoza-Brzezina, M., & Kotłowski, J. (2018). International confidence spillovers and business cycles in small open economies. Empirical Economics, 61(2), 773–798.

DOI: https://doi.org/10.1007/s00181-020-01887-3
View in Google Scholar

Camacho, M., & Garcia-Serrador, A. (2011). The Euro-sting revisited, PMI versus ESI to obtain Euro area GDP forecasts. BBVA Research, Working Papers, 1120.
View in Google Scholar

Cesaroni, T., & Iezzi, S. (2017). The predictive content of business survey indicators: Evidence from SIGE. Journal of Business Cycle Research, 13(1), 75–104.

DOI: https://doi.org/10.1007/s41549-017-0015-8
View in Google Scholar

Cizmesija, M., & Skrinjaric, T. (2021). Economic sentiment and business cycles: A spillover methodology approach. Economic Systems, 45(3), 100770.

DOI: https://doi.org/10.1016/j.ecosys.2020.100770
View in Google Scholar

Cizmesija, M., & Soric, P. (2010). Assessing Croatian GDP components via economic sentiment indicator. Ekonomska Istrazivanja, 23(4), 1–10.

DOI: https://doi.org/10.1080/1331677X.2010.11517429
View in Google Scholar

Clar, M., Duque, J. C., & Moreno, R. (2007). Forecasting business and consumer surveys indicators–a time-series models competition. Applied Economics, 39(20), 2565–2580.

DOI: https://doi.org/10.1080/00036840600690272
View in Google Scholar

Claveria, O., Monte, E., & Torra, S. (2016). Quantification of survey expectations by means of symbolic regression via genetic programming to estimate economic growth in central and Eastern European economies. Eastern European Economics, 54(2), 171–189.

DOI: https://doi.org/10.1080/00128775.2015.1136564
View in Google Scholar

Dritsaki, C. (2015). Forecasting real GDP rate through econometric models: An empirical study from Greece. Journal of International Business and Economics, 3(1), 13–19.

DOI: https://doi.org/10.15640/jibe.v3n1a2
View in Google Scholar

Erkisi, K., & Tekin, U. E. (2019). The relationship between intermediate and capital goods imports, industrial production and economic growth: The case of Turkey. Yaşar Üniversitesi E-Dergisi, 14(55), 358–368.

DOI: https://doi.org/10.19168/jyasar.516702
View in Google Scholar

Everts, M. P. (2006). Measuring business cycles. Berlin: Verlag im Internet GmbH.

DOI: https://doi.org/10.2139/ssrn.905804
View in Google Scholar

Fernandes, N. (2020). Economic effects of coronavirus outbreak (COVID-19) on the world economy. IESE Business School Working Paper, WP-1240-E.

DOI: https://doi.org/10.2139/ssrn.3557504
View in Google Scholar

Ferreira, E., Martínez Serna, M.I., Navarro, E., & Rubio, G. (2008). Economic sentiment and yield spreads in Europe. European Financial Management, 14(2), 206–221.

DOI: https://doi.org/10.1111/j.1468-036X.2007.00389.x
View in Google Scholar

Garnitz, J., Lehmann, R., & Wohlrabe, K. (2019). Forecasting GDP all over the world using leading indicators based on comprehensive survey data. Applied Economics, 51(54), 5802–5816.

DOI: https://doi.org/10.1080/00036846.2019.1624915
View in Google Scholar

Gelper, S., & Croux, C. (2010). On the construction of the European economic sentiment indicator. Oxford Bulletin of Economics and Statistics, 72(1), 47–62.

DOI: https://doi.org/10.1111/j.1468-0084.2009.00574.x
View in Google Scholar

Guerrieri, V., Lorenzoni, G., Straub, L., & Werning, I. (2020). Macroeconomic implications of COVID-19: Can negative supply shocks cause demand shortages? NBER Working Paper, 26918.

DOI: https://doi.org/10.3386/w26918
View in Google Scholar

Gyomai, G., & Guidetti, E. (2012). OECD system of composite leading indicators. OECD Statistics Working Paper, 2012/5.
View in Google Scholar

Hüfner, F. P., & Schroder, M. (2002). Forecasting economic activity in Germany - How useful are sentiment indicators? ZEW Discussion Paper, 2–56.

DOI: https://doi.org/10.2139/ssrn.339141
View in Google Scholar

Jorda, O., Singh, S. R., & Taylor, A. M. (2020). Longer-run economic consequences of pandemics. NBER Working Paper, 26934.

DOI: https://doi.org/10.3386/w26934
View in Google Scholar

Kanapickienė, R., Teresienė, D., Budrienė, D., Keliuotytė-Staniulėnienė, G., & Kartašova, J. (2020). The impact of COVID-19 on European financial markets and economic sentiment. Economy & Business, 14(1), 144–163.
View in Google Scholar

Kolková, A., & Ključnikov, A. (2022). Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - The case of SMEs and large enterprises. Economics & Sociology, 15(4), 39–62.

DOI: https://doi.org/10.14254/2071-789X.2022/15-4/2
View in Google Scholar

Kovacic, Z., & Vilotic, M. (2017). Characterising and testing European business cycles asymmetry. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(3), 453–468.

DOI: https://doi.org/10.24136/eq.v12i3.24
View in Google Scholar

Lee, H. S. (2020). Exploring the initial impact of COVID-19 sentiment on US stock market using big data. Sustainability, 12(16), 6648.

DOI: https://doi.org/10.3390/su12166648
View in Google Scholar

Lemmens, A., Croux, C., & Dekimpe, M. G. (2007). Consumer confidence in Europe: United in diversity? International Journal of Research in Marketing, 24(2), 113–127.

DOI: https://doi.org/10.1016/j.ijresmar.2006.10.006
View in Google Scholar

Lemmens, A., Croux, C., & Dekimpe, M.G. (2005). On the predictive content of production surveys: A pan-European study. Internationa Journalof Forecasing, 21(2), 363–375.

DOI: https://doi.org/10.1016/j.ijforecast.2004.10.004
View in Google Scholar

Lipkind, T., Kitrar, L., & Ostapkovich, G. (2019). Russian business tendency sur-veys by HSE and Rosstat. In S. Smirnov, A. Ozyildirim & P. Picchetti (2019). Business cycles in BRICS. Societies and political orders in transition (pp. 233–251). Cham: Springer.

DOI: https://doi.org/10.1007/978-3-319-90017-9_13
View in Google Scholar

Mazurek, J., & Mielcova, E. (2017). Is Consumer Confidence Index a suitable predictor of future economic growth? An evidence from the USA. E&M Ekonomie a Mnagement, 20(2), 30–45.

DOI: https://doi.org/10.15240/tul/001/2017-2-003
View in Google Scholar

Michis, A. A. (2021). Wavelet multidimensional scaling analysis of European Economic Sentiment Indicators. Journal of Classification, 38(3), 443–480.

DOI: https://doi.org/10.1007/s00357-020-09380-3
View in Google Scholar

Nilsson, R., & Brunet, O. (2006). Composite leading indicators for major OECD non-member economies: Brazil, China, India, Indonesia, Russian Federation, South Africa. OECD Publishing.
View in Google Scholar

Ojo, M. O., Aguiar‐Conraria, L., & Soares, M. J. (2023). The performance of OECD's composite leading indicator. International Journal of Finance & Economics, 28(2), 1–13.

DOI: https://doi.org/10.1002/ijfe.2784
View in Google Scholar

Olkiewicz, M. (2022). The impact of economic indicators on the evolution of business confidence during the COVID-19 pandemic period. Sustainability, 14(9), 5073.

DOI: https://doi.org/10.3390/su14095073
View in Google Scholar

Pawęta, B. (2018). Analysis of the economic cycles of Poland in years 1996–2017. Finanse i Prawo Finansowe, 2(18), 51–64.

DOI: https://doi.org/10.18778/2391-6478.2.18.05
View in Google Scholar

Plakandaras, P., Kumar Tiwari, A., & Gupta, R. (2019). Spillover of sentiment in the European Union, evidence from time- and frequency-domains. International Review of Economics and Finance, 68(C), 105–130.

DOI: https://doi.org/10.1016/j.iref.2020.03.014
View in Google Scholar

Raoufina, K. (2016). Forecasting employment growth in Sweden using a Bayesian VAR Model. National Institute of Economic Research. Working Paper, 144.
View in Google Scholar

Schilcht, E. (2005). Estimating the smoothing parameter in the so-called Hodrick-Prescott filter. Journal of the Japanese Statistical Society, 35(1), 99–119.

DOI: https://doi.org/10.14490/jjss.35.99
View in Google Scholar

Simionescu, M., & Giedrė Raišienė, A. (2021). A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states? Technological Forecasting and Social Change, 173.

DOI: https://doi.org/10.1016/j.techfore.2021.121170
View in Google Scholar

Škare, M., & Stjepanović, S. (2016). Measuring business cycles: A review. Contemporary Economics, 10(1), 83–94.

DOI: https://doi.org/10.5709/ce.1897-9254.200
View in Google Scholar

Skikiewicz, R., & Blonski, K. (2018). Economic sentiment level versus the quality of life in European union member states. Prague Economic Papers, 27(4), 379–396.

DOI: https://doi.org/10.18267/j.pep.658
View in Google Scholar

Soric, P. (2018). Consumer confidence as a GDP determinant in New EU Member States, a view from a time-varying perspective. Empirica, 45(2), 261–282.

DOI: https://doi.org/10.1007/s10663-016-9360-4
View in Google Scholar

Soric, P., Lolic, I., & Cizmesija, M. (2016). European economic sentiment indicator, an empirical reappraisal. Quality & Quantity, 50(5), 2025–2054.

DOI: https://doi.org/10.1007/s11135-015-0249-2
View in Google Scholar

Teresiene, D., Keliuotyte-Staniuleniene, G., Liao, Y., Kanapickiene, R., Pu, R., Hu, S., & Yue, X.-G. (2021). The impact of the COVID-19 pandemic on consumer and business confidence indicators. Journal of Risk Financial Management, 14(4), 159.

DOI: https://doi.org/10.3390/jrfm14040159
View in Google Scholar

Wang, X., Xu, Z., Qin, Y., & Skare, M. (2021). Service networks for sustainable business: A dynamic evolution analysis over half a century. Journal of Business Research, 13(6), 543–557.

DOI: https://doi.org/10.1016/j.jbusres.2021.07.062
View in Google Scholar

Zervoyianni, A., Dimelis, S., & Livada, A. (2023). Economic sentiment and the Covid-19 crisis: Evidence from European countries. Applied Economics, 55(1), 113–130.

DOI: https://doi.org/10.1080/00036846.2022.2061903
View in Google Scholar

Zhang, H., Ding, Y., & Li, J. (2021). Impact of the COVID-19 pandemic on economic sentiment: A cross-country study. Emerging Markets Finance and Trade, 57(6), 1603–1612.

DOI: https://doi.org/10.1080/1540496X.2021.1897005
View in Google Scholar

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Published

2023-09-30

How to Cite

Tkacova, A., & Gavurova, B. (2023). Economic sentiment indicators and their prediction capabilities in business cycles of EU countries. Oeconomia Copernicana, 14(3), 977–1008. https://doi.org/10.24136/oc.2023.029

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