Cycle Duration in Production with Periodicity – Evidence from Turkey

Year 2018, Volume: 10 Issue: 2, 24 - 32, 01.09.2018

Yilmaz Akdi Serdar Varlik , Hakan Berument

https://doi.org/10.33818/ier.440103

Cited By: 1

Abstract

In this paper, the sub-cycles
in two economic activity measures are analyzed
by using periodogram analyses. Our
results from Turkish data suggest that industrial production and capacity
utilization rate consist of various cycles including seasonal cycles.
These series also have common cycles
which correspond about four and seven years. Last, industrial production has also additional cycles compare to capacity utilization
ratio cycle.

Keywords

Business cycle, Spectral Analysis

References

• Akdi, Y. and D. A. Dickey (1998). Periodograms of unit root time series: distributions and test.Communications in Statistics-Theory and Methods, 27(1), 69-87.
• Baxter, M. and R. G. King (1999). Measuring business cycles: approximate band-pass filters foreconomic time series. Review of Economics and Statistics, 81(4), 575-593.
• Beveridge, S. and C. R. Nelson (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(2), 151-174.
• Brockwell, P. J. and R. A. Davis (1987). Time series: theory and methods. Springer Science & Business Media.
• Bry, G. and C. Boschan (1971). Standard business cycle analysis of economic time series.In Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, NBER Books, 64-150.
• Burns, A. F. and W. C. Mitchell (1946). Measuring business cycles. NBER Books.
• Campbell, J. Y. and N. G. Mankiw (1987). Are output fluctuations transitory?. The Quarterly Journalof Economics, 102(4), 857-880.
• Castro, V. (2010). The duration of economic expansions and recessions: More than durationdependence. Journal of Macroeconomics, 32(1), 347-365.
• Chib, S. (1993). Bayes regression with autoregressive errors: A Gibbs sampling approach. Journal ofEconometrics, 58(3), 275-294.Christiano, L. J. and T. J. Fitzgerald (2003). The bandpass filter. International Economic Review, 44(2), 435-465.
• Clark, P. K. (1987). The cyclical component of US economic activity. The Quarterly Journal ofEconomics, 102(4), 797-814.
• Cogley, T. and J. M. Nason (1995). Effects of the Hodrick-Prescott filter on trend and differencestationary time series Implications for business cycle research. Journal of Economic Dynamicsand Control, 19(1-2), 253-278.
• Diebold, F. X. and G. D. Rudebusch (1990). A nonparametric investigation of duration dependence inthe American business cycle. Journal of Political Economy, 98(3), 596-616.
• Diebold, F. X., G. Rudebusch and D. Sichel (1993). Further evidence on business-cycle durationdependence. In Business Cycles, Indicators and Forecasting, University of Chicago Press, 255-284.
• Durland, J. M. and T. H. McCurdy (1994). Duration-dependent transitions in a Markov model of USGNP growth. Journal of Business & Economic Statistics, 12(3), 279-288.
• Filardo, A. J. (1994). Business-cycle phases and their transitional dynamics. Journal of Business &Economic Statistics, 12(3), 299-308.
• Fuller, W.A. (1996). Introduction to Statistical Time Series, 2nd Edition, Willey, USA.
• Granger, C. W., T. Terasvirta and H. M. Anderson (1993). Modeling nonlinearity over thebusiness cycle. In Business cycles, indicators and forecasting, University of Chicago Press, 311-326.
• Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series andthe business cycle. Econometrica: Journal of the Econometric Society, 357-384.
• Harvey, A. C. (1985). Trends and cycles in macroeconomic time series. Journal of Business & Economic Statistics, 3(3), 216-227.
• King, R. G. and C. I. Plosser (1994). Real business cycles and the test of the Adelmans. Journal ofMonetary Economics, 33(2), 405-438.
• King, R. G. and S. T. Rebelo (1993). Low frequency filtering and real business cycles. Journal ofEconomic Dynamics and Control, 17(1-2), 207-231.
• McCulloch, R. E. and R. S. Tsay (1994). Statistical analysis of economic time series via Markovswitching models. Journal of Time Series Analysis, 15(5), 523-539.
• Mitchell, W. C. (1927). The processes involved in business cycles. In Business cycles: the problem andits setting, NBER Books.
• Mitchell, W. C. and A. F. Burns (1938). Statistical indicators of cyclical revivals. In StatisticalIndicator of Cyclical Revivals, NBER Books, 1-12.
• Nelson, C. R. and C. R. Plosser (1982). Trends and random walks in macroeconmic time series: someevidence and implications. Journal of Monetary Economics, 10(2), 139-162.
• Ohn, J., L., W. Taylor and A. Pagan (2004). Testing for duration dependence in economic cycles. TheEconometrics Journal, 7(2), 528-549.
• Prescott, E. C. (1986). Theory ahead of business-cycle measurement. In Carnegie-RochesterConference Series on Public Policy, North-Holland, 25, 11-44.
• Singleton, K. J. (1988). Econometric issues in the analysis of equilibrium business cyclemodels. Journal of Monetary Economics, 21(2-3), 361-386.
• Stock, J. H. and M. W. Watson (1999). Business cycle fluctuations in US macroeconomic time series. Handbook of Macroeconomics, 1, 3-64.
• Watson, M. W. (1986). Univariate detrending methods with stochastic trends. Journal of MonetaryEconomics, 18(1), 49-75.
• Watson, M. W. (1992). Business cycle durations and postwar stabilization of the US economy,NBER Working Papers, 4005.
• Wei, W. W. S. (2006). Time Series Analysis, Univariate and Multivariate Methods, 2nd edition, Pearson Education, USA.