Application of cross-spectral analysis to research in dynamics of economic processes in Germany
DOI:
https://doi.org/10.29015/cerem.199Keywords:
spectrum, cross-spectrum, squared coherency, cross-amplitude, phase spectrumAbstract
Spectral analysis serves recognition of harmonic structure of time series. Cross-spectral analysis is in fact a joint and simultaneous spectral analysis of two time series. Establishing connections and relations between two time series appearing in different frequencies is the aim of cross-spectral analysis.
The phase spectrum (phase shift) of the industrial production, the employment and the forecast of the economic activity of the industrial production of Germany demonstrate the linear and positive relation for low frequencies (in which we observe considerable values for the squared coherency). It would suggest that the changes of long waves in the employment and in forecasts of the economic activity of industrial production remain ahead of the appropriate changes in the dynamics of industrial production of Germany. We observe a similar relationship in case of the phase DAX spectrum and unemployment. However, in case of the phase DAX spectrum and prices of industrial goods the linear relationship is negative, which suggests lag changes in prices of industrial goods in relation to changes of a DAX share index.
For considerable values of the squared coherency we observe a negative linear relation of the phase spectrum of two time series which suggests lag phases of manufacture of cement in relation to economic activity indicators in the construction in the area of low frequencies.
The carried out cross-spectral research shows that in the analyzed period the dynamics of industrial production of Germany and the dynamics of DAX share index are correlated with the dynamics of chosen economic processes in different frequencies of trade cycle. It is possible then to talk about symptoms of correlated cyclical behaviors. Revealing the diversified phase spectrums and gains in low as well as in high frequencies of cyclical fluctuations, the examined time series in German economy deserves special attention. Therefore, cross-spectral analysis can be an effective tool supporting the process of constructing barometers of economic activity, the policy of stabilization or economic recovery.References
Baillie R. T., Long memory processes and fractional integration in econometrics, „Journal of Econometrics” nr 73, 1996, s. 5–59.
Banerjee A., Urga G. (2004), Modelling Structural Breaks, Long Memory and Stock Market Volatility: An Overview, CEA@Cass Working Paper Series, WP-CEA-07-2004, http://www.cass.city.ac.uk/cea/index.html (17.08.2014).
Beran J. (1994), Statistics for long memory processes, Chapman and Hall, New York.
Blackman R. B., Tukey J.W. (1959), The Measurement of Power Spectra from the Point of View of Communication Engineering, Dover, New York.
Bloomfield P. (1976), Fourier analysis of time series: An introduction, Wiley, New York.
Brigham E.O. (1974), The fast Fourier transform, Prentice-Hall, Englewood Cliffs, NJ.
Brillinger D. R. (1975), Time series: Data analysis and theory, Holt, Rinehart & Winston, New York.
Childers D. G. (Ed.) (1978), Modern Spectrum Analysis, IEEE Press, New York.
Cohen L. (1995), Time-Frequency Analysis, Prentice Hall, Englewood Cliffs, NJ.
Deo R., Hsieh M., Hurvich C.M. (2005), Tracing the Source of Long Memory in Volatility, http://129.3.20.41/eps/em/papers/0501/0501005.pdf, s. 1–38 (25.08.2014).
Dickey D., Pantula S. (1987), Determining the order of differencing in autoregressive processes, „Journal of Business and Economic Statistics”, nr 15, s. 455–461.
Elliott D.F., Rao K.R. (1982), Fast transforms: Algorithms, analyses, applications, Academic Press, New York.
Fouet M. (1981), Analyser la conjoncture, Hatier, Paris.
Geweke J., Porter-Hudak S. (1983), The Estimation and Application of Long Memory Time Series Models, „Journal of Time Series Analysis”, nr 4, s. 221–228.
Hurst H. E. (1951), Long-term Storage of Reservoirs, „Transactions of the American Society of Civil Engineers”, nr 116, s. 770–799.
Jenkins G. M., Watts D.G. (1968), Spectral analysis and its applications, Holden-Day, San Francisco.
Kay S. M. (1988), Modern Spectral Estimation, Theory and Application, Prentice Hall, Englewood Cliffs, NJ.
Kesler S. B. (Ed.) (1986), Modem Spectrum Analysis II. New York, IEEE Press.
Koopmans L. H. (1974), The Spectral Analysis of Time Series, New York, Academic Press.
Lo A. W. (1991), Long-Term Memory in Stock Market Prices, „Econometrica”, nr 59(5), s.1279–1313.
Long memory and nonlinear time series, (Eds) Davidson J., Terasvirta T.T. (2002), „Journal of Econometrics”, nr 110, Issue 2, s. 105–437.
Łuczyński W. (1998), Analiza dynamiki procesów gospodarczych Niemiec w latach 1949–1996, Wyd. AE, Poznań.
Łuczyński W. (2007), Estymacja mocy gęstości spektralnej za pomocą algorytmu cyfrowej analizy widmowej MUSIC [w:] Handel i finanse międzynarodowe w warunkach globalizacji (red. J.Schroeder, B. Stępień), Wyd. AEP, Poznań, s. 137–148.
Łuczyński W., The long memory dynamics of the market quotations of selected Stock Companies and Warsaw Stock Index, „Poznań University of Economics Review”, 2007, vol. 7, nr 1 s. 21–55.
Łuczyński W., Zastosowania analizy harmonicznej i spektralnej oraz analizy przeskalowanego zakresu w badaniu realnych cykli koniunkturalnych, „Ekonomista”, 1998, nr 5–6, s. 629–647.
Marple L. (1987), Digital Spectral Analysis with Applications, Prentice Hall, New Jersey, Englewood Cliffs, NJ.
Mitra S. K., Kaiser J. (1993), Handbook for Digital Signal Processing, John Wiley and Sons, Inc., New York.
Naidu P. S. (1996), Modern Spectrum Analysis of Time Series, CRC Press, Boca Raton, FL.
Pantula S. G. (1989), Testing for Unit Roots in Time Series Data, „Econometric Theory”, Vol. 5, nr 2, s. 256–271.
Parzen E. (1961), Mathematical Considerations in the Estimation of Spectra, „Technometrics”, v. 3, nr 2, s. 167–190.
Percival D. B., Constantine W.L.B. (2005), Exact Simulation of Gaussian Time Series from Nonparametric Spectral Estimates with Application to Bootstrapping, „Journal of Computational and Graphical Statistics”, accepted for publication.
Percival D. B., Walden A. (1993), Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques, Cambridge University Press, Cambridge.
Priestley M. B. (1981), Spectral analysis and time series, Academic Press, New York.
Rinne H., Specht K. (2002), Zeitreihen. Statistische Modellierung, Schätzung und Prognose, Verlag Franz Vahlen, München.
Robinson P. M. (2003), Long memory time series, w: Time Series with Long Memory, (ed.) Robinson P. M., Oxford University Press, Oxford.
Riedel K. S., Sidorenko A., Minimum bias multiple taper spectral estimation, „IEEE Transactions on Signal Processing”, nr 43, 1995, s. 188–195.
Schuster A., On the Investigation of Hidden Periodicities with Application to a Supposed 26-Day Period of Meteorological Phenomena, „Terr. Mag. Atmos. Elect.”, nr 3, 1998, s. 13–41.
Shumway R. H. (1988), Applied statistical time series analysis, Prentice Hall, Englewood Cliffs, NJ.
Stock J. H., Watson M. W., Business cycle fluctuations in US macroeconomic time series, „NBER Working Paper Series”, nr 6528, 1998.
Stoica P., Moses R. L. (1997), Introduction to Spectral Analysis, Prentice-Hall, Upper Saddle River, New Jersey.
Stryjkowski T. (2013), Implementacja testu na istotność poszczególnych częstości periodogramu w programie GRETL [w:] Metody i zastosowania ekonometrii współczesnej (Red. naukowa M. Kośko), Wyd. Uczelniane WSIiE TWP, Olsztyn, s. 145–158.
Syczewska E. M. (2002), Analiza relacji długookresowych: estymacja i weryfikacja, SGH w Warszawie, Warszawa.
Talaga L., Zieliński Z. (1986), Analiza spektralna w modelowaniu ekonometrycznym, PWN, Warszawa.
Thomson D. J., Spectrum estimation and harmonic analysis, „Proceedings of the IEEE”, nr 72(9), 1982, s. 1055–1096.
Walden A. T., Accurate Approximation of a 0th Order Discrete Prolate Spheroidal Sequence for Filtering and Data Tapering, „Signal Processing”, nr 18, 1989, s. 341–348.
Wei W. W. (1989), Time series analysis: Univariate and multivariate methods, Addison-Wesley, New York.
Weron A., Weron R. (1998), Inżynieria finansowa. Wycena instrumentów pochodnych. Symulacje komputerowe. Statystyka rynku, WNT, Warszawa.
Zieliński T. P. (2007), Cyfrowe przetwarzanie sygnałów. Od teorii do zastosowań, WKiŁ, Warszawa.
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