Discrete Spectral Analysis. The Case of Industrial Production in Selected European Countries
Keywordsdiscrete spectral analysis, almost periodic function, frequency identification, graphical test
AbstractThe aim of this paper is to show the usefulness the discrete spectral analysis in identification cyclical fluctuations. The subsampling procedure was applied to construct the asymptotically consistent test for Fourier coefficient and frequency significance. The case of monthly production in industry in European countries (thirty countries) was considered. Using proposed approach the frequencies concerning business fluctuations, seasonal fluctuations and trading-day effects fluctuations were recognized in considered data sets. The comparison with existing procedures was shown.
Croux, Ch., Forni, M. and Reichlin, L. (2001), A measure of covomement for economic variables: theory and empirics. The Review of Ecomonics and Statistics, 83(2):232–241, DOI: http://dx.doi.org/10.1162/00346530151143770.
Doukhan, P., (1994), Mixing: Properties and Examples. Springer-Verlag, New York,
Ftiti, Z., (2010), The macroeconomic performance of the inflation targeting policy: An approach based on the evolutionary co–spectral analysis (extension for the case of a multivariate process). Economic Modelling, 27:468–476,
Guyon, X., (1995), Random Fields on a Network. Springer-Verlag, New York.
Hamilton J.D., (1994), Time Series Analysis. Princeton University Press, New Jersey.
Hurd, H. and Gerr L., (1991), Graphical methods for determining the presence of periodic correlation. J. Time Ser. Anal., 12(4):337–350. DOI: http://dx.doi.org/10.1111/j.1467-9892.1991.tb00088.x.
Ladiray, D., (2012), Theoretical and real trading-day frequencies, in Bell W.R., Holan S.H. and McErloy T.S. (ed.), Economic time Series: Modeling and Seasonality, pages 255–279. DOI: http://dx.doi.org/10.1201/b11823-16.
Lenart Ł, Pipień M., (2013a), Almost periodically correlated time series in business fluctuations analysis. Acta Physica Polonica A, 123(3):567–583. DOI: http://dx.doi.org/10.12693/APhysPolA.123.567.
Lenart Ł, Pipień M., (2013b), Seasonality revisited - statistical testing for almost periodically correlated processes. Central European Journal of Economic modelling and Econo-metrics, 5:85–102.
Lenart Ł, Pipień M., (2015), Testing the common length of the business cycles with discrete spectral analysis and subsampling approach. Submited to Journal of Time Series Analysis.
Lenart Ł., (2011), Asymptotic distributions and subsampling in spectral analysis for almost periodically correlated time series. Bernoulli, 17(1):290–319. DOI: http://dx.doi.org/10.3150/10-BEJ269.
Lenart Ł., (2013), Non-parametric frequency identification and estimation in mean function for almost periodically correlated time series. Journal of Multivariate Analysis, 115:252–269. DOI: http://dx.doi.org/10.1016/j.jmva.2012.10.006.
Li T.H. and Song K.S.., (2002), Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation IEEE Transactions on Information Theory, 48(10):2709 – 2720
McAdam P. and Mestre R.., (2008), Evaluating macro–economic models in the frequency domain: A note. Economic Modelling, 25:1137–1143.
Metz R., (2009), Comment on ”Stock markets and business cycle comovement in Germany before world war I: Evidence from spectral analysis”. Journal of Macroeconomics, 31:58–67, DOI: http://dx.doi.org/10.1016/j.jmacro.2008.01.004.
Orlov A.G., (2006), Capital controls and stock market volatility in frequency domain. Eco-nomics Letters, 91:222–228, DOI: http://dx.doi.org/10.1016/j.econlet.2005.09.014.
Orlov A.G., (2009), A cospectral analysis of exchange rate comovements during Asian finan-cial crisis. Journal of International Financial Markets, Institutions and Money, 19:742–758, DOI: http://dx.doi.org/10.1016/j.intfin.2008.12.004.
Pakko M.R., (2004), A spectral analysis of the cross–country consumption correlation puzzle. Economics Letters, 84:341–347.
Priestley M.B.,., (1981), Spectral Analysis and Time Series. Academic Press, London, DOI: http://dx.doi.org/10.2307/2983035.
Uebele M. and Ritschl.A., (2009), Stock markets and business cycle comovement in Germany before World War I: Evidence from spectral analysis. Journal of Macroeconomics, 31:35–57.
How to Cite
The journal provides an Open Access to its content based on the non-exclusive licence Creative Commons (CC BY-ND 4.0).
To enable the publisher to disseminate the author's work to the fullest extent, the author must agrees to the terms and conditions of the License Agreement with Nicolaus Copernicus University.
Number of views and downloads: 125
Number of citations: 0