How Regional Business Cycles Diffuse through Space and Time: Evidence from Spatial Markov Model of Polish NUTS-3 Regions

• Autorzy: Agnieszka Rabiej , Dominika Sikora-Kruszka , Andrzej Torój
• Języki publikacji: EN

Abstrakty

EN

In a national economy, are individual subnational regions business cycle takers or setters? We address this important regional policy question by investigating regional business cycles at NUTS-3 granularity in Poland (N = 73), using two metrics in parallel: GDP dynamics and unemployment. To extract the business cycle, we use a spatial Markov switching model that features both idiosyncratic business cycle fluctuations across regions (as a 2-state chain), as well as spatial interactions with other regions (as spatial autoregression). The posterior distribution of the parameters is simulated with a Metropolis-within-Gibbs procedure. We find a clear division into business cycle setters and takers, the latter being largely (but not only) non-metropolitan regions. (original abstract)

Słowa kluczowe

EN

Business cycles; Markov models; Spatial analysis; Nomenclature of Territorial Units for Statistics (NUTS)

PL

Cykl koniunkturalny; Modele Markowa; Analiza przestrzenna; Klasyfikacja NUTS

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2023
• Tom: 15
• Numer: nr 4
• Strony: 345--383

Twórcy

autor

Agnieszka Rabiej

• Reckitt

autor

Dominika Sikora-Kruszka

• EY

autor

Andrzej Torój

• SGH Warsaw School of Economics, Poland

Bibliografia

• [1] Anselin L., Florax R., (1994), Small sample properties of tests for spatial dependence in regression models: some further results, Regional Res. Inst. Research Paper, Morgantown, WV: Regional Research Institute, 9414.
• [2] Artis M., Dreger C., Kholodilin K., (2011), What drives regional business cycles? The role of common and spatial components, The Manchester School 79(5), 10351044.
• [3] Beraja M., Hurst E., Ospina J., (2016), The Aggregate Implications of Regional Business Cycles, NBER Working Papers, 21956.
• [4] Bernardelli M., Dędys M., (2015), Przelącznikowe modele Markowa w analizie synchronizacji cykli koniunkturalnych, Roczniki Kolegium Analiz Ekonomicznych SGH 39(14), 213-227.
• [5] Chauvet M., Hamilton J., (2005), Dating Business Cycle Turning Points, NBER Working Papers, 11422.
• [6] Chauvet M., Piger J., (2003), Identifying business cycle turning points in real time, Federal Reserve Bank of St. Louis. Review 85(2), 47-61.
• [7] Chib S., (2001), Markov chain Monte Carlo methods: computation and inference, Chapter 57, [in:] Handbook of Econometrics, vol. 5, [eds.:] J. Heckman, E. Leamer, Elsevier, Amsterdam, 3569-3649.
• [8] Clements M., Krolzig H., (1998), A Comparison of the Forecast Performance of Markov-switching and Threshold Autoregressive Models of US GNP, The Econometrics Journal 1(1), 47-75.
• [9] Diebold F., Lee J., Weinbach G., (1994), Regime switching with time-varying transition probabilities, Nonstationary Time Series Analysis and Cointegration, 283-302.
• [10] Diebold F., Rudebusch G., (1996), Measuring Business Cycles: A Modern Perspective, The Review of Economics and Statistics 78(1), 67-77.
• [11] Drozdowicz-Bieć M, (2008), Regionalne cykle koniunkturalne. Doświadczenia światowe - implikacje dla Polski, Barometr Regionalny 3(13), 5-15.
• [12] Dueker M., (1997), Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility, Journal of Business and Economic Statistics 15(1), 2634.
• [13] Fatas A., (1997), EMU: Countries or Regions? Lessons from the EMS Experience, CEPR Discussion Papers, 1558.
• [14] Filardo A., Gordon S., (1998), Business cycle durations, Journal of Econometrics 85(1), 99-123.
• [15] Frankel J., Rose A., (1998), The Endogeneity of the Optimum Currency Area Criteria, The Economic Journal 108(449), 1009-1025.
• [16] Fruhwirth-Schnatter S., (2001), Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models, Journal of the American Statistical Association 96, 194-209.
• [17] Fruhwirth-Schnatter S., Kaufmann S., (2008), Model-Based Clustering of Multiple Time Series, Journal of Business & Economic Statistics 26, 78-89.
• [18] Hamilton J., (1989), A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle, Econometrica 57(2), 357-384.
• [19] Hamilton J., Owyang M., (2012), The Propagation of Regional Recessions, The Review of Economics and Statistics 94(4), 935-947.
• [20] Hansen B., (1992), The Likelihood Ratio Test Under Nonstandard Conditions: Testing the Markov-Switching Model of GNP, Journal of Applied Econometrics 7(S1), S61-S82.
• [21] Harding D., Pagan A., (2006), Synchronization of cycles, Journal of Econometrics 132(1), 59-79.
• [22] Kim C., Murray C., (2002), Permanent and transitory components of recessions, [in:] Advances in Markow-Switching Models, Studies in Empirical Economics, [eds.:] J. D. Hamilton, B. Raj, Physica Heidelberg, 19-39.
• [23] Kim C., Piger J., Startz R., (2004), Estimation of Markov Regime-Switching Regression Models with Endogenous Switching, Federal Reserve Bank of St. Louis, Working Papers, 15.
• [24] Kim C. J., Nelson C., (1998), Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching, Review of Economics and Statistics 80(2), 188-201.
• [25] Kim C. J., Nelson C., (1999), Has the U.S. economy become more stable? A Bayesian approach based on a Markov-switching model of the business cycle, Review of Economics and Statistics 81(4), 608-616.
• [26] Kondo K., (2021), Spatial Dependence in Regional Business Cycles: Evidence from Mexican States, Discussion Paper Series Research Institute for Economic and Business Administration Kobe University, DP2015-27, available at https: //www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2015-27.pdf.
• [27] Kośko M., Kwiecień M., Stempińska J., (2016), Przełącznikowe modele Markowa (MS) - charakterystyka i sposoby zastosowań w badaniach ekonomicznych, Roczniki Kolegium Analiz Ekonomicznych SGH 40, 479-490.
• [28] Krolzig H., (1997), Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis, Berlin, Springer.
• [29] Krugman P., (1990), Increasing Returns and Economic Geography, NBER Working Papers, 3275.
• [30] Lawrence J. C., Trabandt M., Walentin K., (2021), Involuntary unemployment and the business cycle, Review of Economic Dynamics 39, 26-54.
• [31] Marelli E., (2007), Specialisation and Convergence in European Regions, European Journal of Comparative Economics 4(2), 149-178.
• [32] Owyang M., Piger J., Wall H., (2005), Business Cycle Phases in U.S. States, The Review of Economics and Statistics 87(4), 604-616.
• [33] Owyang M., Wall H., (2006), Regional Vars and the Channels of Monetary Policy, Federal Reserve Bank of St. Louis, Working Paper Series.
• [34] Podgórska M., Decewicz A., (2001), Modele Markowa w analizie wyników testu koniunktury, Prace i Materiały Instytutu Rozwoju Gospodarczego SGH 70, 119156.
• [35] Shibaev S., (2016), Recession propagation in small regional economies: Spatial spillovers and endogenous clustering, Queen's Economics Department, Working Paper, 1369.
• [36] Sims C., Zha T., (2006), Were There Regime Switches in U.S. Monetary Policy?, The American Economic Review 96(1), 54-81.
• [37] Stock J., Watson M., (1989), New Indexes of Coincident and Leading Economic Indicators, NBER Macroeconomics Annual 4, 351-409.
• [38] Stock J., Watson M., (1991), A Probability Model of the Coincident Economic Indicators, Chapter [in:] The Leading Economic Indicators: New Approaches and Forecasting Records, [eds.:] G. Moore, K. Lahiri, Cambridge University Press, 63-90.
• [39] Torój A., (2020), Metody ekonometryczne, statystyczne i matematyczne w modelowaniu zjawisk społecznych, Tom II: Metody ekonometryczne w makro- i mikroekonomii, chapter Regionalne cykle rentowności polskich przedsiębiorstw: bayesowska analiza przestrzennego modelu przełącznikowego, Oficyna Wydawnicza SGH, 181-196.
• [40] Wall H., (2007), Regional Business Cycle Phases in Japan, Federal Reserve Bank of St. Louis. Review 89(1), 61-76.
• [41] Warżała R., (2011), Analiza koniunktury gospodarczej w ujęciu regionalnym na przykładzie województwa warmińsko-mazurskiego, Studia Regionalne i Lokalne 3(45), 81-99.
• [42] Warżała R., (2014): Regionalne cykle koniunkturalne w Polsce w latach 20002014, Zeszyty Naukowe Uniwersytetu Szczecińskiego. Studia i Prace Wydziału Nauk Ekonomicznych i Zarządzania 35(2), 529-550.
• [43] Warżała R., (2015), Specialization and business cycles fluctuations of Polish regions, Managerial Economics 16(2), 175-188.

Identyfikatory

DOI

10.24425/cejeme.2023.149072