Titlebook: Business Cycles; An International Com Ulrich Woitek Book 1997 Physica-Verlag Heidelberg 1997 Germany.Konjunkturzyklen.Wirtschaftsgeschichte

上下倒置

Book 1997n) novel in the analysis of economic time series is described and utilized. The method turns out to be superior to widely used time domain methods and the "classical" spectral estimate, the periodogram. The results for eleven OECD countries confirm and extend the basic set of stylized facts of tradi

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https://doi.org/10.1007/978-3-658-15889-7ince it efficiently extracts the information on the cyclical structure contained in very short time series. The application of the ME-spectrum to the analysis of business cycles was pioneered at SEMECON, University of Munich, but thus far only for univariate series. The present study extends this approach to the multivariate case.

歡呼

Introductionince it efficiently extracts the information on the cyclical structure contained in very short time series. The application of the ME-spectrum to the analysis of business cycles was pioneered at SEMECON, University of Munich, but thus far only for univariate series. The present study extends this approach to the multivariate case.

紅腫

Business Cycles978-3-642-48856-6Series ISSN 1431-1933 Series E-ISSN 2197-7178

砍伐

Measuring Electronics and Sensorscomputed, given by where . denotes the sample mean. This function measures the degree of the linear relationship between two observations in the same series. High values of the autocovariance function at lag r indicate that the series exhibits cyclical structure with a cycle length of about r units of time.

originality

Mohammad Zaman,Lee Heng,Christoph Müllerty. Since the publication of the influential book by Box and Jenkins (1970), the time-domain approach is prevailing in the description of business cycle stylized facts. Looking at modern papers in this research field, the application of spectral analysis methods is rather the exception than the rule. What is the reason for this development?

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Conclusionctral estimation is superior to the widely used time domain methods by providing far more detailed results. Moreover, this estimate is more suitable for the short macroeconomic time series than the normally used spectral estimate, the periodogram.