Perilaku Siklus Bisnis Dalam Tinjauan Fraktal
DOI:
https://doi.org/10.33603/e.v10i1.8464Abstract
Tujuan paper ini adalah untuk menyelidiki perilaku siklus bisnis dengan pendekatan fraktal. Data yang digunakan digunakan dalam penelitian adalah PDB riil dari 1983 – 2017 per kuartal yang bersumber dari Badan Pusat Statistik. Metode yang digunakan untuk mengekstraksi siklus adalah filter Hodrick-Prescott dengan penentuan smoothing parameter optimal, kemudian dimensi fraktal untuk menyelidiki perilaku siklus bisnis. Hasil yang diperoleh dari penelitian ini adalah diperoleh dimensi fraktal siklus sebesar 1.548, artinya bahwa siklus bisnis tidak murni berperilaku white noise sehingga fitting model dapat diterapkan guna menggali informasi. Namun, dalam proses fitting model tersebut akan mengalami kendala karena keberadaan efek shock krisis yang kuat.
References
Batten, J. & Ellis, C. (1996). Fractal structures and naive trading systems: Evidence from the spot US dollar/Japanese yen. Japan and the World Economy, 8(4), pp: 411-421.
Chatterjee, S. & Yilmaz, M. (1992). Use of estimated fractal dimension in model identification for time series. Journal of Statistical Computation and Simulation, 41:3-4, 129-141. DOI: 10.1080/00949659208811397
Choudhary, A., Hanif, N. & Iqbal, J. (2013). On Smoothing Macroeconomic Time series Using HP and Modified HP Filter. MPRA Paper No. 45630.
Fajar, M., Darwis, S. and Gumgum Darmawan, G. (2017). Spectral analysis and markov switching model of Indonesia business cycle. AIP Conference Proceedings 1827.) https://doi.org/10.1063/1.4979447
Hall, P. & Wood, A. (1993). On the performance of box-counting estimators of fractal dimension. Biometrika 80(1): pp. 246-251.
Hodrick, R.J. & Prescott, EC. (1997). Post-war U.S Business Cycles: An Empirical Investigation. Journal of Money, Credit, and Banking 29, pp: 1 – 16.
Jacobs, J. (1998). Econometric Business Cycle Research [1 ed.]. US: Springer.
Mandelbrot, B.B. (1971). A fast Fractional Gaussian Noise Generator. Water Resource Research, 7, pp: 543-553.
Mandelbrot, B. B. (1982). The Fractal Geometry of Nature. San Francisco: W.H. Freeman and Co.
Mandelbrot. B. B. & Van Ness, J. W. (1968). Fractional Brownian Motions, Fractional Noises and Applications. S l A M Review 10, pp: 422-37.
Peters, E. E. (1994). Fractal market analysis: applying chaos theory to investment and economics (Vol. 24). New York: Wiley.
Richards, G. R. (2000). The fractal structure of exchange rates: Measurement and forecasting. Journal of international Financial Markets, Institutions and Money, 10(2), pp: 163-180.
Phillips, P.C.B. & Jin, S. (2015). Business Cycles, Trend Elimination, and the HP filter. Yale Univeristy, Cowles Foundation Discussion Paper No. 2005.
Stock, J.H. & Watson, M.W. (1998). Business Cycle Fluctuation in US Macroeconomic Time Series. Melalui <http://www.nber.org/papers/w6528.pdf> [07/09/16]
Tokinaga, S., Moriyasu, H., Miyazaki, A., & Shimazu, N. (1999). A forecasting method for time series with fractal geometry and its application. Electronics and Communications in Japan. (Part III: Fundamental Electronic Science), 82(3), 31–39. doi:10.1002/(sici)1520-6440(199903)82:3<31::aid-ecjc4>3.0.co;2-h
Wei, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods. California: Pearson Education, Inc.
Zarnowitz, V. (1992). Business Cycles: Theory, History, Indicators, and Forecasting, volume 27 of National Bureau of Economic Research Studies in Business Cycles. Chicago: The University of Chicago Press.
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