Ex-Ante Analysis of Macro-Regional Development in the Visegrad Countries, with Special Emphasis on Some Turbulent Periods

• Autorzy: György Kocziszky , Dóra Szendi

Warianty tytułu

Analiza ex-ante rozwoju makroregionalnego w krajach Grupy Wyszehradzkiej, ze szczególnym uwzględnieniem wybranych okresów gwałtownych zmian

• Języki publikacji: EN

Abstrakty

EN

Subject and purpose of work: Monitoring the development of a given region and forecasting its potential changes is an evergreen topic in regional economic analysis. The aim of the current work is to analyse the development path of four Central-Eastern-European countries and create short term forecast for their development. Materials and methods: The authors discuss and test an autoregressive model for short-run, ex-ante assessment of spatial development using data from four CEE countries (Poland, Czech Republic, Slovakia, Hungary). Results: The research shows that the initial (1995-2021) development trajectories of the countries were still determined by the shocks of the transition period that started after 1990. The analysis shows that further development is essentially determined by inflationary pressures and changes in fiscal and monetary conditions. Conclusions: The analysis shows that after a recovery period of 1.5-2 years, the countries could be back on the path of development from 2024 but starting from a lower level and at a more modest pace. (original abstract)

Przedmiot i cel pracy: Monitorowanie rozwoju danego regionu i prognozowanie jego potencjalnych zmian jest odwiecznym tematem w regionalnej analizie ekonomicznej. Celem niniejszej pracy jest analiza ścieżki rozwoju czterech krajów Europy Środkowo-Wschodniej i stworzenie krótkoterminowej prognozy ich rozwoju. Materiały i metody:: Autorzy omawiają i testują model autoregresyjny do krótkookresowej oceny ex ante rozwoju przestrzennego z wykorzystaniem danych z czterech krajów Europy Środkowo-Wschodniej (Polska, Czechy, Słowacja, Węgry). Wyniki: Badanie pokazuje, że początkowe (1995-2021) trajektorie rozwoju krajów były nadal determinowane przez wstrząsy okresu transformacji, który rozpoczął się po 1990 roku. Analiza pokazuje, że dalszy rozwój jest zasadniczo zdeterminowany przez presję inflacyjną oraz zmiany warunków fiskalnych i monetarnych. Wnioski: Analiza pokazuje, że po okresie ożywienia trwającym 1,5-2 lata, kraje mogą powrócić na ścieżkę rozwoju od 2024 r., ale zaczynając od niższego poziomu i w mniejszym tempie. (abstrakt oryginalny)

Słowa kluczowe

EN

Macroeconomic situation; Forecasting; Econometrics; Economic development of regions; Autoregression models

PL

Sytuacja makroekonomiczna; Prognozowanie; Ekonometria; Rozwój gospodarczy regionów; Modele autoregresji

• Czasopismo: Economic and Regional Studies
• Rocznik: 2023
• Tom: 16
• Numer: nr 2
• Strony: 147--170

Twórcy

autor

György Kocziszky

• University of Miskolc, Hungary

autor

Dóra Szendi

• University of Miskolc, Hungary

Bibliografia

• Ackermann, R. (2001). Pfadabhängigkeit, Institutionen und Regelkonform. Mohr Siebeck, Tübingen (in German).
• Adamowicz, M. (2020). Conditions of Local Development in the Context of Shaping Developmental Strategy and Policy in Poland. Economic and Regional Studies, 13(2), 145-169. https://doi.org/10.2478/ers-2020-0011
• Barto, A., Dietterich, T. G. (2004). Reinforcement Learning and its Relationship to Supervised Learning. Handbook of Learning and Approximate Dynamic Programming, 47-64.
• Benedek, J. (2021). Regionális egyenlőtlenség és gazdasági felzárkózás. Magyarországi és romániai régiók összehasonlító vizsgálata. (Regional inequality and economic catching-up. Comparative analysis of regions in Hungary and Romania) Észak-magyarországi Stratégiai Füzetek, 18(1), 4-14 (in Hungarian). https://doi.org/10.32976/stratfuz.2021.15
• Berndt, E. R. (1991). The Practice of Econometrics: Classic and Contemporary. Addison Wesley, Reading.
• BFS (2022). Indikatoren der Lebensqualität. Bundesamt für Statistik (in German).bfs.adminch/bfs/de
• Biau, O., D'elia, A. (2010). Euro Area GDP Forecasting Using Large Survey Datasets: A Randon Forest Approach. Euro indicators Working Papers.
• BMZ (2012). Territoriale Entwicklung im ländlichen Raum. Bundesministerium für wirtschaftliche Zusammenarbeit. Berlin (in German).
• Burla, L., Widmer, M., Zeitner, C. (2022). Zukünftiger Bestand und Bedarf an Fachärztinnen und -ärzten in der Schweiz. OBSAN Bericht, Neuchätel (in German).
• Cripps, F. (2014). Macro-model scenarios and implications for European policy. in Eatwell, John, Terry McKinley, and Pascal Petit, eds. Challenges for Europe in the World of 2030. Farnham, Surrey: Ashgate Publishing.
• Dijkstra, L., Annoni, P., Kozovska, K. (2011). A New Regional Competitiveness Index: Theory, Methods and Findings. European Union Regional Policy Working Papers No. 2.
• EC (2020). Handbook of Territorial and Local Development Strategies. European Commission, Brussels.
• Eckey, H. F., Schwengler, B., Türck, M. (2007). Vergleich von deutschen Arbeitsmarktregionen. IAB-Discussion Paper, No. 3/2007 (German).
• EU (2012). Europäische Erhebung zur Lebensqualität (EQLS). European Foundation for the Improvement of Living and Working Conditions (in German). https://www.eurofound.europa.eu/de/surveys/european-quality-of-life-surveys
• EU (2015a). Agenda 2000. Eine stärkere und erweiterte Union (in German). https://eur-lex.europa.eu/DE/legal-content/summary/agenda-2000-for-a-stronger-and-wider-union.html
• EU (2015b). Politische Strategien für ein besseres Leben. (in German). https://www.europarl.europa.eu/summits/lis1_de.htm
• EU (2018). Die 28 Mitgliedstaaten der EU. Brüssel (in German). https://european-union.europa.eu/principles-countries-history/country-profiles_de
• Eurostat (2017). Statistiken zur Gesundheitsversorgung. (in German).
• Fehr, H. (2011). Berechnungen des langfristigen Produktionspotenzials unter Berücksichtigung verschiedener altersbedingter und bildungsspezifischer Produktivitätsprofile. Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, Wiesbaden, Arbeitspapier Nr. 02 (in German).
• Fenz, G., Spitzer, M. (2005). AQM: The Austrian Quarterly Model of the Oesterreichische Nationalbank. Working Papers 104. Österreichische Nationalbank, Wien. (in German).
• Gillmann, N., Lehmann, R., Nauerth, J. A., Ponattu, D., Ragnitz, J., Sonnenburg, J., Weber, M. (2019). Wachstum und Produktivität 2035. ifo Institut, Dresden Studien, Nr. 84. (in German).
• Headey, B., Muffels, R., Wooden, M. (2008). Wealth and happiness revisited - growing national income does go with greater happiness. Social Indicators Research, 64, pp. 1-27.
• Heilemann, U. (2009). Déja Vu: Prognose in der Kriese. Wirtschaftsdienst, Nr. 2, pp. 90-95. (in German).
• Heilemann, U. (2019). Rezessionen in der Bundesrepublik Deutschland von 1966 bis 2013. Wirtschaftsdienst, Nr. 8, pp. 546-551. https://doi.org/10.1007/s10273-019-2489-6. (in German).
• Hesse, M., Meyerhoff, J., Petschow, U. (1998). Verkehrsinfrastrukturen und wirtschaftliche Entwicklung. Institut für ökologische Wirtschaftsforschungs, Berlin. (in German).
• IAB (2010). Regionale Arbeitsmarktprognosen der Arbeitslosen und sozialversicherungspflichtig Beschäftigten. Nr. 2, Nürnberg. (in German).
• ITM (2020). Nemzeti energia- és klímaterv. Innovációs és Technológiai Minisztérium, Budapest. (in Hungarian).
• Jung, J. K., Patnam, M., Ter-Martirosyan, A. (2018). An Algorithmic Crystal Ball: Forecasts-based on Machine Learning. IMF Working Paper No. 2018/230, pp. 1-33. https://doi.org/10.5089/9781484380635.001
• Kirchgässner, G., Savioz, M. (2001). Monetary Policy and Forecasts for Real GDP Growth. German Economic Review. 2 (4), pp. 339-365. https://doi.org/10.1111/1468-0475.00044
• Kocziszky, Gy., Benedek, J. (2012). Contributions to the issues of regional economic growth and equilibrium as well as the regional policy. Hungarian Geographical Bulletin 61 (2), 113-130.
• Kocziszky, Gy., Szendi, D. (2021). Comparative Analysis of the Economic Development Paths of Hungarian Counties. PUBLIC FINANCE QUARTERLY 66 : Special edition, 2021/2, pp. 7-30. https://doi.org/10.35551/PFQ_2021_s_2_1
• Kocziszky, Gy., Szendi, D. (2023). Köztes európai (V4+2) országok fejlődési pályáinak összehasonlító vizsgálata kompozit index segítségével. Regional Statistics (under publishing). (in Hungarian).
• Koll, W. (2009). Welche Rolle spielen Prognosen? Wirtschaftsdienst, Nr. 2. pp. 79-86. https://doi.org/10.1007/s10273-009-0894-y
• Kozak, S., Grzeda, L. (2019). Quantitative Changes of Endogenous Factors Affecting the Economic Development of the Mazowieckie Voivodeship in 2004-2017. Economic and Regional Studies, Volume 12, No. 3, pp. 326-335. https://doi.org/10.2478/ers-2019-0029
• Kumar, S. M., Woo, J. (2010). Public Dept and Growth. IMF Working Paper. WP/10/174. https://doi.org/10.5089/9781455201853.001
• Martin, R. (2010). Roepke Lecture in Economic Geography - Rethinking Regional Path Dependence: Beyond Lock-in to Evolution. Economic Geography, 86: 1-27. https://doi.org/10.1111/j.1944-8287.2009.01056.x
• Meinel, G., Reichert, S. (2004). Flächenwirkung des deutschen Autobahnnetzes - Konzept und erste Ergebnisse einer GIS-gestützten Analyse. Deutschen Gesellschaft für Photogrammetrie, Band 13. pp. 227-234.
• MNB (2022). Inflation Report. Magyar Nemzeti Bank, Budapest.
• Mourougane, A. (2006). Forecasting Monthly GDP for Canada. OECD Economics Department Working Papers, No. 515.
• OECD (2014). How's Life in Your Region? Measuring Regional and Local Well-being for Policy Making. OECD Publishing, Paris.
• OECD (2022). Economic Outlook. (www.oecd.org/economic-outlook/november-2022).
• Peterson, J. S. (2014). Review of Research at Virginia Tech on the Relationship between School Building Condition and Student and Teacher Performance and Attitudes. PhD dissertation. Virginia Polytechnic Institute and State University. https://vtechworks.lib.vt.edu/bitstream/handle/10919/56836/Peterson_JS_D_2014.pdf?isAllowed=y&sequence=1
• Potrafke, N., Gründler, K., Mosler, M., Dörr, L. (2019). Der Zusammenhang zwischen Verschuldung, Budgetzusammensetzung und volkswirtschaftlichem Wachstum. ifo Forschungs-Berichte, 107/2019. München.
• Sedillot, F., Pain, N. (2003). Indicator Models of Real GDP Growth in Selected OECD Countries. OECD Economics Department Working Papers, No. 364.
• Seto, K. C., Davis, S. J., Mitchell, R. B., Stokes, E. C., Unruh, G., Ürge-Vorsatz, D. (2016). Carbon Lock-In: Types, Causes, and Policy Implications. Annual Review of Environment and Resources, 41 (1), pp. 425-452. https://doi.org/10.1146/annurev-environ-110615-085934
• Starfield, B., Birn, A. E. (2007). Income redistribution is not enough: income inequality, social welfare programs, and achieving equity in health. J Epidemiol Community Health, 61 (12), pp. 1038-1041. https://doi.org/10.1136/jech.2006.054627
• Szendi, D., Kocziszky, Gy. (2022). Changes in the social and technological innovation potential of the Visegrad (V4) regions (2001-2019). Észak-magyarországi Stratégiai Füzetek XIX. 4. pp. 34-47. https://doi.org/10.32976/stratfuz.2022.42
• Tiberius, V., Gojowy, R., Dabic, M. (2022). Forecasting the future of robo advisory: A three-stage Delphi study on economic, technological, and societal implications. Technological Forecasting and Social Change. 182. https://doi.org/10.1016/j.techfore.2022.121824
• Zimmermann, K. F. (2009). Prognosekriese: Warum weniger manchmal mehr ist. Wirtschaftsdienst, Nr. 2, pp. 86-90. (in German).
• https://datatopics.worldbank.org/world-development-indicators/
• https://digital-strategy.ec.europa.eu/en/policies/desi
• https://en.unesco.org/creativity/activities/cdis
• https://hdr.undp.org/content/human-development-report-2020
• https://publicadministration.un.org/egovkb/en-us/About/Overview/-E-Government-Development-Index
• https://sdgs.un.org/goals
• https://www.mnb.hu/letoltes/versenyke-pesse-gi-jelente-s-hun-2021-1018.pdf

Identyfikatory

DOI

10.2478/ers-2023-0011