Spectral Analysis Of Business Cycles In The Visegrad Group Countries

Arkadiusz Kijek

doi:10.1515/cer-2017-0012

Authors

Arkadiusz Kijek Maria Curie-Skłodowska University in Lublin, Faculty of Economics, Department of Statistics and Econometrics

DOI:

https://doi.org/10.1515/cer-2017-0012

Keywords:

business cycle, synchronization, spectral analysis, Fourier representation

Abstract

This paper examines the business cycle properties of Visegrad group countries. The main objective is to identify business cycles in these countries and to study the relationships between them. The author applies a modification of the Fourier analysis to estimate cycle amplitudes and frequencies. This allows for a more precise estimation of cycle characteristics than the traditional approach. The cross-spectral analysis of GDP cyclical components for the Czech Republic, Hungary, Poland and Slovakia makes it possible to assess the degree of business cycle synchronization between the countries.

Downloads

Download data is not yet available.

References

Aguiar-Conraria L., Soares M.J. (2011), Business Cycle Synchronization and the Euro: a Wavelet Analysis, ‘Journal of Macroeconomics’, Vol. 33, 477–489.
Google Scholar

Artis M., Krolzig H.M., Toro J. (2004a), The European Business Cycle, ‘Oxford Economic Papers’, 56, 1–44.
Google Scholar

Artis M.J., Marcellino M., Proietti T. (2004b), Characterizing the business cycle for accession countries, CEPR Discussion Papers 4457.
Google Scholar

Backus D.K., Kehoe P.J., Kydland F.E. (1992), International Real Business Cycles, ‘Journal of Political Economy’, Vol. 100, No. 4, 745–775.
Google Scholar

Bartlett M.S. (1950), Periodogram Analysis and Continuous Spectra, ‘Biometrika’, Vol. 37, No. 1–2, 1–16.
Google Scholar

Baxter M., King R.G. (1995), Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series, Working Paper No. 5022, National Bureau of Economic Research, Cambridge.
Google Scholar

Beveridge S., Nelson C.R. (1981), A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the ‘Business Cycle’, ‘Journal of Monetary Economics’, 7, 151–174.
Google Scholar

Burns A.F., Mitchell W.C. (1946), Measuring Business Cycles, N.Y.: National Bureau of Economic Research, New York.
Google Scholar

Camacho M., Perez-Quiros G., Saiz L. (2008), Do European business cycles look like one?, ‘Journal of Economic Dynamics and Control’, Vol. 32, No. 7, 2165–2190.
Google Scholar

Campbell J.Y., Mankiw N.G. (1987), Permanent and Transitory Components in Macroeconomic Fluctuations, ‘American Economic Review’ (Papers and Proceedings), 77, 111–117.
Google Scholar

Chatfield C. (1996), The Analysis of Time Series: An Introduction, Chapman & Hall, London.
Google Scholar

Christiano L.J., Fitzgerald T.J. (1999), The Band Pass Filter, Working Paper No. 9906, Federal Reserve Bank of Cleveland.
Google Scholar

Clark P.K. (1987), The Cyclical Component of U.S. Economic Activity, ‘The Quaterly Journal of Economics’, Vol. 102, 797–814.
Google Scholar

Crowley P., Lee J. (2005), Decomposing the Co-movement of the Business Cycle: A Time-Frequency Analysis of Growth Cycles in the Euro Area, Bank of Finland discussion papers 12/2005.
Google Scholar

Darvas Z., Szapary G. (2008), Business Cycle Synchronization in the Enlarged EU, ‘Open Economies Review’, Vol. 19, No. 1, 1–19.
Google Scholar

Fidrmuc J., Korhonen I. (2006), Meta-analysis of the Business Cycle Correlation Between the Euro Area and the CEECs, ‘Journal of Comparative Economics’, Vol. 34, No. 3, 518–537.
Google Scholar

Forni M., Hallin M., Lippi M., Reichlin L. (2000), The Generalized Dynamic-factor Model: Identification and Estimation, ‘The Review of Economics and Statistics’, Vol. 82, No. 4, 540–554.
Google Scholar

Hamilton J.D. (1994), Time Series Analysis, Princeton University Press.
Google Scholar

Hanus L., Vacha L. (2015), Business Cycle Synchronization of the Visegrad Four and the European Union, IES Working Paper: 19/2015.
Google Scholar

Harding D., Pagan A. (2006), Synchronization of Cycles, ‘Journal of Econometrics’, Vol. 132, No. 1, 59–79.
Google Scholar

Harvey A.C. (1985), Trends and Cycles in Macroeconomic Time Series, ‘Journal of Business and Economic Statistics’, Vol. 3, 216–227.
Google Scholar

Harvey A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge, New York and Melbourne.
Google Scholar

Harvey A.C. (2000), Trends Analysis, University of Cambridge, Faculty of Economics and Politics.
Google Scholar

Hodrick R., Prescott E. (1980), Post-War U.S. Business Cycles: An Empirical Investigation, Working paper, Carnegie Mellon University.
Google Scholar

Hodrick R.J., Prescott E.C. (1997), Postwar U.S. Business Cycles: An Empirical Investigation, ‘Journal of Money Credit and Banking’, Vol. 29, No. 1, 1–16.
Google Scholar

Inotai A., Sass M. (1994), Economic Integration of Visegrad Countries. Facts and Scenarios, ‘Eastern European Economics’, Vol. 32, No. 6, 6–23.
Google Scholar

Kaposzta J., Nagy H. (2015), Status Report about the Progress of the Visegrad Countries in Relation to Europe 2020 Targets, ‘European Spatial Research and Policy’, Vol. 22, No. 1, 81–99.
Google Scholar

Kydland F., Prescott C. (1990), Business Cycles: Real Facts and A Monetary Myth, Federal Reserve Bank of Minneapolis, Quarterly Review (Spring), 3–18.
Google Scholar

Lee J. (2012), Measuring Business Cycle Comovements in Europe: Evidence from a Dynamic Factor Model with Time-varying Parameters, ‘Economics Letters’, Vol. 115, No. 3, 438–440.
Google Scholar

Lucas R.E. (1977), Understanding Business Cycles, [in:] Brunner K., Meltzer A. H. (eds.), Stabilization of the Domestic and International Economy, Carnegie-Rochester Conference Series on Public Policy 5, Amsterdam: North Holland, 7–29.
Google Scholar

Nelson C.R., Plosser C.I. (1982), Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications, ‘Journal of Monetary Economics’, 10, 139–162.
Google Scholar

Parzen E. (1961), Mathematical considerations in the estimation of spectra: Comments on the discussion of Messers, Tukey, and Goodman, ‘echnometrics’, 3, 167–190; 232–234.
Google Scholar

Priestley M.B. (1981), Spectral Analysis and Time Series, Vols 1 and 2, Academic Press, London.
Google Scholar

Stock J., Watson M. (2005), Understanding Changes in International Business Cycle, ‘Journal of European Economic Association’, Vol. 3, No. 5, 968–1006.
Google Scholar

Stock J.H., Watson M.W. (1988), Variable Trends in Economic Time Series, ‘Journal of Economic Perspectives’, Vol. 2, 147–174.
Google Scholar

Warner R.M. (1998), Spectral Analysis of Time Series Data, ‘The Guilford Press’, New York.
Google Scholar

Watson M.W. (1986), Univariate Detrending Methods with Stochastics Trends, ‘Journal of Monetary Economics’, Vol. 18, No. 1, 49–75.
Google Scholar

Zarnowitz V., Ozyildirim A. (2001), Time Series Decomposition and Measurement of Business Cycles, Trends and Growth Cycles, ‘Economics Program Working Paper Series’, The Conference Board, New York.
Google Scholar