Prediction of Business Cycle of Poland

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

Abstrakty

EN

The paper is focused on the construction of a new composite indicator intended to predict the economic cycle of Poland and its comparison with the existing CLI used by international institutions such as OECD and Eurostat. In part, this research is also dedicated to monitoring the partial advance cyclical indicators that make up the CLI components and their changes over time. The paper explores 62 qualitative and quantitative economic indicators of Poland and their relationship to the development of monthly GDP at constant prices in three different time periods: 2005 to 2021, 2010 to 2021, and 2016 to 2021. A modified OECD method is used to select the cyclical component of time series using the Hodrick-Prescott filter and subsequently employ cross-correlation of the variables with the cyclical component of GDP. The constructed CLI can predict the evolution of the CLI one month ahead with a cross-correlation level of 0.879 under equal weights and 0.877 under different weights. Research has shown that there is no significant change in the composition of the CLI for the prediction of the economic cycle of Poland when using the established methodology. (original abstract)

Słowa kluczowe

EN

Forecasting; Business cycles; Gross domestic product (GDP)

PL

Prognozowanie; Cykl koniunkturalny; Produkt krajowy brutto (PKB)

• Czasopismo: Journal of International Studies
• Rocznik: 2022
• Tom: 15
• Numer: nr 3
• Strony: 65--81

Twórcy

autor

Andrea Tkacova

• Technical University of Košice, Slovakia

autor

Beata Gavurova

• Tomas Bata University in Zlin, Czech Republic

autor

Katarina Kelemen

• Technical University of Košice, Slovakia

Bibliografia

• Aerle, B., Keppler, M, Seymen, A., & Weyerstrass K. (2012). Economic sentiment shocks and fluctuations in economic activity in the Euro Area. Intereconomics: Review of European Economic Policy, 47(1), 44-51. https://doi.org/10.1007/s10272-012-0405-z.
• Arnoštová, K., Havrlant, D., Růžička, L., & Luboš, T. P. (2011). Short-term forecasting of Czech quarterly GDP using monthly indicators. Finance a Úver, 61(6), 566-583.
• 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, 2016/07. https://doi.org/10.1787/5jlz4gs2pkhf-en
• Bandholz, H. (2005). New Composite Leading Indicators for Hungary and Poland. ifo Working Paper, No. 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, Munich
• Barhoumi, K., Darné, O., & Ferrara, L. (2016). A world trade leading index (WTLI). Economics Letters, 146. https://doi.org/10.1016/j.econlet.2016.07.032.
• Bartos, V., Vochozka, M., & Janíková, J. (2021). Fair value in squeeze-out of large mining companies. Acta Montanistica Slovaca, 26(4), 712-731. https://doi.org/10.46544/AMS.v26i4.10
• Bartoš, V., Vochozka, M. & Šanderová, V. (2022). Copper and Aluminium as Economically Imperfect Substitutes, Production and Price Development. Acta Montanistica Slovaca, 27(2), 462-478. https://doi.org/10.46544/AMS.v27i2.14
• Bašić Bakarić, I., Tkalec, M., & Vizek, M. (2016). Constructing a composite coincident indicator for a post-transition country. Economic Research-Ekonomska Istraživanja, 29(1), 434-445. https://doi.org/10.1080/1331677x.2016.1174388.
• Berge, T. J. (2015). Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle. Journal of Forecasting, 34(6), 455-471. https://doi.org/10.1002/for.2345.
• Bjørnland, H. C., Ravazzolo, F., & Thorsrud, L. A. (2017). Forecasting GDP with global components: this time is different. International Journal of Forecasting, 33(1), 1-23. https://doi.org/10.1016/j.ijforecast.2016.02.004.
• Ciegis, R., Aidas Dilius, A., & Asta Mikalauskiene, A. (2015). Evaluation od Economic Growth in Terms of Sustainability. Transformation in Business and Economics, 14 (1). 105-125.
• Clark, T. E., & Ravazzolo F. (2015). Macroeconomic forecasting performance under alternative specifications of time-varying volatility. Journal of Applied Econometrics, 30(4). 551-575. https://doi.org/10.1002/jae.2379.
• Czesaný, S., & Ježábková, Z. (2009.) Metóda konstrukce kompozitních indikatorú hospodářského cyklu pro českou ekonomiku. Statistika, 89(1), 21-31.
• Czesaný, S., & Ježábková, Z. (2009). Kompozitní indikátory hospodářského cyklu české ekonomiky. Statistika, 89(3), 257-274.
• Czesaný, S., Macháčková, L., & Sedláček, P. (2007). Monitorování a analýza hospodárského cyklu. Praha: Český statistický úřad.
• De Vroey M.R., & Pensieroso L. (2006). Real Business Cycle Theory and the Great Depression: The Abandonment of the Abstentionist Viewpoint. The B.E. Journal of Macroeconomics, 6(1), 1-26. https://doi.org/10.2202/1534-6005.1403.
• Digdowiseiso, K., & Sugiyanto, E. (2021). How effective is institutional quality for the creation of small & medium enterprises (SMEs) in Indonesia?. Economics and Sociology, 14(1), 263-274. https://doi.org/10.14254/2071-789X.2021/14-1/17
• Djogo, M. & Stanišić, N. (2016). Is the Global Competitiveness Report the right measure of macroeconomic competitiveness? Zbornik Radova Ekonomskog Fakulteta u Rijeci / Proceedings of Rijeka School of Economics, 34(1), 91-117. https://doi.org/10.18045/zbefri.2016.1.91.
• Dobeš, K., Kot, S., Kramoliš, J., & Sopková, G. (2017). The Perception of Governmental Support in The Context of Competitiveness of SMEs in the Czech Republic. Journal of Competitiveness, 9 (3), 34-50. https://doi.org/10.7441/joc.2017.03.03.
• Döpke, J., Fritsche, U., & Pierdzioch, C. (2017). Predicting recessions with boosted regression trees. International Journal of Forecasting, 33(4), 745-759. https://doi.org/10.1016/ j.ijforecast.2017.02.003.
• Drozdowicz-Bieć, M. (2001), The Coincident and Leading Index for Poland. New York: Bureau for Investments and Economic Cycles.
• European Comission. (2022). Eurostat's Business Cycle Clock (BCC). A user's guide. European Commision.
• Eurostat, (2017). Handbook on Cyclical Composite indicators, 2017 edition. Luxembourg: Publications Office of the European Union. https://doi.org/10.2785/962890.
• Ebong, J., & Babu, G. (2020). Demand for credit in high-density markets in Kampala: Application of digital lending and implication for product innovation. Journal of International Studies, 13(4), 295-313. doi:10.14254/2071-8330.2020/13- 4/21
• Fabuš, M. (2017). Current development of business environment in Slovakia and Czech Republic. Entrepreneurship and Sustainability Issues, 5(1), 127-137. https://doi.org/10.9770/ jesi.2017.5.1(10).
• Ferrara, L., Marcellino, M., & Mogliani, M. (2015). Macroeconomic forecasting during the great recession: the return of non-linearity? International Journal of Forecasting, 31(3), 667-679. https://doi.org/10.1016/j.ijforecast.2014.11.005.
• Fulop, G., & Gyomai, G. (2012). Transition of the OECD CLI system to a GDP - based business cycle target. Paris: OECD Publishing.
• Garczarczyk, J., & Skikiewicz, R. (2011). Assessment of Composite Leading Indicators Usefulness in Forecasting Loans and Deposits Cyclical Fluctuations. Instytut Rozwoju Gospodarczego (SGH), 86(2), 135-160.
• Gayer, Ch. (2007). Report: The Economic Climat Tracer: A tool to visualise the cyclical stance of the economy using survey data. European Commision.
• Gawel A, Głodowska A. (2021). On the Relationship between Economic Dynamics and Female Entrepreneurship: Reflections for the Visegrad Countries. Administrative Sciences, 11(3):88. https://doi.org/10.3390/admsci11030088
• Gyomai, G. & Guidetti, E. (2012). OECD System of Composite Leading Indicators. Paris: OECD Publishing.
• Jakubiková, E., Banociová, A., & Tkáčová, A. (2014). Kompozitné predstihové indikátory hospodárskych cyklov krajín V4 a ich komparácia s CLI Eurostatu a OECD. Politická Ekonomie, 62(2), 194-215, https://doi.org/10.18267/j.polek.946.
• Jantoń-Drozdowska, E., & Majewska, M. (2016). Investment attractiveness of Central and Eastern European countries in the light of new locational advantages development. Equilibrium. Quarterly Journal of Economics and Economic Policy, 11(1), 97-119. https://doi.org/10.12775/EQUIL.2016.005
• Jovovic, N. (2021). The impact of corruption on competition in the countries of Southeast Europe. Journal of International Studies, 14(4), 87-96. https://doi.org/10.14254/2071-8330.2021/14-4/6
• Kliuchnikava, Y. (2022). The Impact of the Pandemic on Attitude to Innovations of SMEs in the Czech Republic. International Journal of Entrepreneurial Knowledge, 10(1), 34-45. https://doi.org/10.37335/ijek.v10i1.131
• Kľúčik, M. (2009). Composite Reference Series and Composite Leading Indicator for Slovakia. Rome: The First Macroeconomic Forecasting Conference - MFC.
• Kľúčik, M., Haluška, J. (2008). Construction of composite leading indicator for Slovak economy. Stiin Ńe Economice, 55, 362-370.
• Korsakienė, R., Diskienė, D., & Smaliukienė, R. (2015). Institutional theory perspective and internationalization of firms. How institutional context influences internationalization of SMES? Entrepreneurship and Sustainability Issues, 2(3), 142-153. doi:10.9770/ jesi.2014.2.3(3).
• Korsakienė, R., Liučvaitienė, A., Bužavaitė, M., & Šimelytė, A. (2017). Intellectual capital as a driving force of internationalization: a case of Lithuanian SMEs. Entrepreneurship and Sustainability Issues, 4(4), 502-515. https://doi.org/10.9770/jesi.2017.4.4(8)
• Kovacic, Z. & Vilotic, M. (2017). Characterising and testing European business cycles asymmetry. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(3), 453-468. https://doi.org/10.24136/eq.v12i3.24.
• Kramoliš, J. (2015). Design as a Condition for Prosperity in Czech Businesses - A Comparative Study. Journal of Competitiveness, 7 (4), 33-47. https://doi.org/10.7441/ joc.2015.04.03.
• Lajtkepová, E. (2016). Differences and similarities in the indebtedness of EU member states after last financial crisis. Oeconomia Copernicana, 7(4), 551-563. https://doi.org/10.12775/OeC.2016.031.
• Lenart, Ł., Mazur, B., & Pipień, M. (2016). Statistical Analysis of Business Cycle Fluctuations in Poland Before and After the Crisis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 11(4), 769-783. https://doi.org/10.12775/EQUIL.2016.035.
• Mazur, B. (2017). Probabilistic predictive analysis of business cycle fluctuations in Polish economy. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(3), 435-452. https://doi.org/10.24136/eq.v12i3.23.
• Mishchuk, H., Yurchyk, H., & Bilan, Y. (2018). Shadow incomes and real inequality within the framework of leadership and social change. In Leadership for the Future Sustainable Development of Business and Education (pp. 89-101). Springer, Cham. https://doi.org/10.1007/978-3-319-74216-8_10
• Monni, S., Palumbo, F., & Tvaronavičienė, M. (2017). Cluster performance: an attempt to evaluate the Lithuanian case. Entrepreneurship and Sustainability Issues, 5(1), 43-57. doi:10.9770/jesi.2017.5.1(4).
• Nilsson, R. (2006). Composite Leading Indicators and Growth Cycles in Major OECD Non-Member Economies and recently new OECD Members Countries. Paris: OECD Publishing. https://doi.org/10.1787/118143571177
• Nilsson, R., & Brunet, O. (2006). Composite Leading Indicators for Major OECD Non-Member Economies: Brazil, China, India, Indonesia, Russian Federation, South Africa. Paris: OECD Publishing.
• OECD (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. Paris: OECD Publishing.
• Oliinyk, O., Bilan, Y., Mishchuk, H. (2021). Knowledge Management and Economic Growth: The Assessment of Links and Determinants of Regulation. Central European Management Journal, 29(3), 20-39. https://doi.org/10.7206/cemj.2658-0845.52
• PawętaB. (2018). Impact of the Global Financial Crisis on the Business Cycle in the Visegrad Group. Entrepreneurial Business and Economics Review, 6(3), 43-58. https://doi.org/10.15678/EBER.2018.060303
• Roszko-Wójtowicz E., & Białek J. (2016). A multivariate approach in measuring innovation performance. Journal of Economics and Business, 34 (2), 443-479. https://doi.org/10.18045/zbefri.2016.2.443.
• Sachpazidu-Wójcicka, K. (2017). Innovation as a determinant of the competitiveness of Polish enterprises. Oeconomia Copernicana, 8(2), 287-299. https://doi.org/10.24136/oc.v8i2.18.
• Saisana, M. & Tarantola, S., (2002). State-of-the-art Report on Current Methodologies and Practices for Composite Indicator Development. European Commission.
• Saltelli, A. (2006) Composite indicators between analysis and advocacy. Social indicators research, 81 (1), 65-77. https://doi.org/10.1007/s11205-006-0024-9.
• Schilcht, E. (2005). Estimating the smoothing parameter in the so-called Hodrick-Prescott filter. Journal of the Japanese Statistical Society, 35(1), 99-119.
• Šegota, A., Tomljanović, M., & Huđek, I. (2017). Contemporary approaches to measuring competitiveness - the case of EU member states. Journal of Economics and Business, 35 (1), 123-150. https://doi.org/10.18045/zbefri.2017.1.123.
• Tkáčová, A., & Kišová, V. (2017). Zmeny zloženia kompozitného predstihového indikátora Slovenska v čase. Politická Ekonomie, 65(5), 583-600. https://doi.org/10.18267/j.polek.1163.
• Travkina, I. (2015). Export and GDP Growth in Lithuania: Short-run or Middle-run Causality? Entrepreneurship and Sustainability, Issues 3(1), 74-84. https://doi.org/10.9770/ jesi.2015.2.4(7).
• Trimbur, T. M. (2006). Detrending economic time series: a Bayesian generalization of the Hodrick-Prescott filter. Journal of Forecasting, 25(4), 247-273. doi:10.14254/2071- 789x.2015/8-2/10.
• Vochozka, M., Kalinová, E., Gao, P. & Smolíková, L. (2021). Development of copper price from July 1959 and predicted development till the end of year 2022. Acta Montanistica Slovaca, 26(2), 262-280. https://doi.org/10.46544/AMS.v26i2.07
• Vochozka, M., Horák, J., Krulický, T. & Pardal, P. (2020). Predicting future Brent oil price on global markets. Acta Montanistica Slovaca, 25(3), 375-392. https://doi.org/10.46544/AMS.v25i3.10
• Vraná, L. (2018). On extending composite leading indicators by international economic series. Statistika. 98(2). 113-13
• Wachira, E.W. (2022). Analysis of Austria's Entrepreneurial Ecosystem Based on the Gei Approach. International Journal of Entrepreneurial Knowledge, 10(1), 123-136. https://doi.org/10.37335/ijek.v10i1.156
• Wasiluk, A., & Ginevičius, R. (2020). Pro-innovative motives for establishing cooperation by enterprises: An empirical study in Poland. Economics and Sociology, 13(2), 258-278. https://doi.org/10.14254/2071-789X.2020/13-2/17
• Zalewski, K. (2009). Forecasting Turning Points with Composite Leading Indicators - the Case of Poland. Ekonomia Journal, 24, 61-9.

Identyfikatory

DOI

10.14254/2071-8330.2022/15-3/5