Determination of Period on Baxter King Filter for Indonesian Business Cycle

Muhammad Fajar; Celine Ghefira Nurhaliza

doi:10.30598/parameterv5i2pp219-232

Muhammad Fajar Peneliti pada Badan Pusat Statistika, Indonesia
Celine Ghefira Nurhaliza

DOI: https://doi.org/10.30598/parameterv5i2pp219-232

Keywords: Business Cycle, Singular Spectrum Analysis, Baxter-King Filter, GDP, Indonesia

Abstract

This study investigates the appropriate period range for the Baxter-King filter in the context of the Indonesian business cycle. The standard parameter of 6 to 32 quarters commonly used in the Baxter-King filter was originally derived from the United States postwar business cycle, and is therefore considered inappropriate when applied directly to Indonesian economic data. To address this issue, Singular Spectrum Analysis (SSA) is employed to empirically identify the cyclical components of Indonesian real GDP, using seasonally adjusted quarterly data spanning from 1983 Q1 to 2016 Q2. The SSA decomposition yields ten principal components, of which nine exhibit sinusoidal patterns with periodicities exceeding four quarters, confirming their classification as genuine business cycle components rather than seasonal fluctuations. These components reveal multiple cyclical frequencies occurring in conjugate pairs as well as distinct shorter periodicities, collectively spanning from 13.40 to 67.00 quarters substantially wider than the conventional range assumed in the standard Baxter-King framework. This empirically derived interval is therefore recommended as a more suitable period range for the Baxter-King filter when analyzing the Indonesian business cycle.

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References

[1] A. F. Burns and W. C. Mitchell, Measuring business cycles, no. burn46-1. National bureau of economic research, 1946.
[2] J. Jacobs, Econometric business cycle research. Springer Science & Business Media, 1998.
[3] R. J. Hodrick and E. C. Prescott, “Postwar US business cycles: an empirical investigation,” J. Money Credit Bank., pp. 1–16, 1997.
[4] M. Baxter and R. G. King, “Measuring business cycles: approximate band-pass filters for economic time series,” Review of economics and statistics, vol. 81, no. 4, pp. 575–593, 1999.
[5] T. J. Fitzgerald and L. J. Christiano, The band pass filter. National Bureau of Economic Research, 1999.
[6] N. Golyandina, V. Nekrutkin, and A. A. Zhigljavsky, Analysis of time series structure: SSA and related techniques. CRC press, 2001.
[7] N. Golyandina, A. Korobeynikov, and A. Zhigljavsky, Singular Spectrum Analysis with R. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. doi: 10.1007/978-3-662-57380-8.
[8] M. Fajar, “Analisis Spektral dan Model Markov Switching Siklus Bisnis Indonesia [Tesis],” Bandung: Universitas Padjadjaran, 2017.
[9] W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods, 2nd edition, 2006. Greg Tobln, 2006.
[10] M. de Carvalho and A. Rua, “Real-time nowcasting the US output gap: Singular spectrum analysis at work,” Int. J. Forecast., vol. 33, no. 1, pp. 185–198, Jan. 2017, doi: 10.1016/j.ijforecast.2015.09.004.
[11] A. Groth, M. Ghil, S. Hallegatte, and P. Dumas, “IDENTIFICATION AND RECONSTRUCTION OF OSCILLATORY MODES IN U.S. BUSINESS CYCLES USING MULTIVARIATE SINGULAR SPECTRUM ANALYSIS,” 2011.
[12] M. de Carvalho, P. C. Rodrigues, and A. Rua, “Tracking the US business cycle with a singular spectrum analysis,” Econ. Lett., vol. 114, no. 1, pp. 32–35, Jan. 2012, doi: 10.1016/j.econlet.2011.09.007.
[13] C. Leon, “Decomposition of the european gdp based on singular spectrum analysis,” 2015.
[14] B. A. Harahap, P. Bary, and A. C. M. Kusuma, “The Determinants of Indonesia’s Business Cycle,” Journal of Central Banking Theory and Practice, vol. 9, no. s1, pp. 215–235, Jul. 2020, doi: 10.2478/jcbtp-2020-0029.
[15] M. K. Anastasia, P. U. Ginting, R. F. Syahrir, O. Hermansyah, and M. Shiroth, “THE DYNAMIC OF INDONESIA’S SECTORAL ECONOMIC CYCLES,” Bulletin of Monetary Economics and Banking, vol. 26, no. 2, pp. 136–167, May 2023, doi: 10.59091/1410-8046.2068.