Economic Sentiment Indicators and Their Prediction Capabilities in Business Cycles of EU Countries

• Autorzy: Andrea Tkacova , Beata Gavurova
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

Economy; Business cycles; Gross domestic product (GDP)

PL

Gospodarka; Cykl koniunkturalny; Produkt krajowy brutto (PKB)

• Czasopismo: Oeconomia Copernicana
• Rocznik: 2023
• Tom: 14
• Numer: nr 3
• Strony: 977--1008

Twórcy

autor

Andrea Tkacova

• Technical University of Košice, Slovakia

autor

Beata Gavurova

• Technical University of Košice, Slovakia

Bibliografia

• Adamowicz, E., & Walczyk, K.. (2013). New EU countries after the great recession. Transformations in Business & Economics, 1(2B), 255-265.
• 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: 10.1016/j.econlet.2021.109730.
• Altin, M., & Uysal, M. (2014). Economic sentiment indicator as a demand determinant. Tourism Analysis, 19(5), 581-597. doi: 10.3727/108354214x14116690097855.
• 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: 10.24818/amp/2020.34-08.
• 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: 10.1016/j.jpolmod.2012.01.010.
• 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. doi: 10.1787/18152031.
• 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: 10.24818/18423264/52.2.18.05.
• 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.
• Bonadio, B., Huo, Zh., Levchenko, A., & Pandalai-Nayar, N. (2020). Global supply chains in the pandemic. Journal of International Economics, 133, 1-23,. doi: 10.1016/j.jinteco.2021.103534.
• Brzoza-Brzezina, M., & Kotłowski, J. (2018). International confidence spillovers and business cycles in small open economies. Empirical Economics, 61(2), 773-798. doi: 10.1016/j.jinteco.2021.103534.
• Camacho, M., & Garcia-Serrador, A. (2011). The Euro-sting revisited, PMI versus ESI to obtain Euro area GDP forecasts. BBVA Research, Working Papers, 1120.
• 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: 10.1007/s41549-017-0015-8.
• Cizmesija, M., & Skrinjaric, T. (2021). Economic sentiment and business cycles: A spillover methodology approach. Economic Systems, 45(3), 100770. doi: 10.1016/j.ecosys.2020.100770.
• Cizmesija, M., & Soric, P. (2010). Assessing Croatian GDP components via economic sentiment indicator. Ekonomska Istrazivanja, 23(4), 1-10. doi: 10.1080/1331677x.2010.11517429.
• 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: 10.1080/00036840600690272.
• 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: 10.1080/00128775.2015.1136564.
• 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: 10.15640/jibe.v3n1a2.
• 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: 10.19168/jyasar.516702.
• Everts, M. P. (2006). Measuring business cycles. Berlin: Verlag im Internet GmbH.
• Fernandes, N. (2020). Economic effects of coronavirus outbreak (COVID-19) on the world economy. IESE Business School Working Paper, WP-1240-E. doi: 10.2139/ssrn.3557504.
• Ferreira, E., Martinez Serna, M.I., Navarro, E., & Rubio, G. (2008). Economic sentiment and yield spreads in Europe. European Financial Management, 14(2), 206-221. doi: 10.1111/j.1468-036x.2007.00389.x.
• 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: 10.2139/ssrn.3409137.
• 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: 10.1002/for.1125.
• 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: 10.3386/w26918.
• Gyomai, G., & Guidetti, E. (2012). OECD system of composite leading indicators. OECD Statistics Working Paper, 2012/5. doi: 10.1787/5k4dhnzw8xr3-en.
• Hufner, F. P., & Schroder, M. (2002). Forecasting economic activity in Germany - How useful are sentiment indicators? ZEW Discussion Paper, 2-56. doi: 10.2139/ssrn.339141.
• Jorda, O., Singh, S. R., & Taylor, A. M. (2020). Longer-run economic consequences of pandemics. NBER Working Paper, 26934. doi: 10.3386/w26934.
• 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.
• Kolkova, 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: 10.14254/2071-789X.2022/15-4/2.
• 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: 10.24136/eq.v12i3.24.
• Lee, H. S. (2020). Exploring the initial impact of COVID-19 sentiment on US stock market using big data. Sustainability, 12(16), 6648. doi: 10.3390/su12166648.
• 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: 10.1016/j.ijresmar.2006.10.006.
• 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: 10.1016/j.ijforecast.2004.10.004.
• Lipkind, T., Kitrar, L., & Ostapkovich, G. (2019). Russian business tendency surveys 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: 10.1007/978-3-319-90017-9_13.
• 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: 10.15240/tul/001/2017-2-003.
• Michis, A. A. (2021). Wavelet multidimensional scaling analysis of European Economic Sentiment Indicators. Journal of Classification, 38(3), 443-480. doi: 10.1007/s00357-020-09380-3.
• Nilsson, R., & Brunet, O. (2006). Composite leading indicators for major OECD nonmember economies: Brazil, China, India, Indonesia, Russian Federation, South Africa. OECD Publishing.
• 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: 10.1002/ijfe.2784.
• 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: 10.3390/su14095073.
• Pawęta, B. (2018). Analysis of the economic cycles of Poland in years 1996-2017. Finanse i Prawo Finansowe, 2(18), 51-64. doi: 10.18778/2391-6478.2.18.05.
• 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: 10.1016/j.iref.2020.03.014
• Raoufina, K. (2016). Forecasting employment growth in Sweden using a Bayesian VAR Model. National Institute of Economic Research. Working Paper, 144.
• 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: 10.14490/jjss.35.99.
• 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: 10.1016/j.techfore.2021.121170.
• Škare, M., & Stjepanović, S. (2016). Measuring business cycles: A review. Contemporary Economics, 10(1), 83-94. doi: 10.5709/ce.1897-9254.200.
• 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: 10.18267/j.pep.658.
• 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: 10.1007/s10663-016-9360-4.
• Soric, P., Lolic, I., & Cizmesija, M. (2016). European economic sentiment indicator, an empirical reappraisal. Quality & Quantity, 50(5), 2025-2054. doi: 10.1007/s11135-015-0249-2.
• 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: 10.3390/jrfm14040159.
• 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: 10.1016/j.jbusres.2021.07.062.
• 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: 10.1080/00036846.2022.2061903.
• 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: 10.1080/1540496X.2021.1897005.

Identyfikatory

DOI

10.24136/oc.2023.029