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Comparison of EMD and HP Filter for Cycle Extraction with Korean Macroeconomic Indices

순환성분 추출을 위한 EMD와 HP 필터의 비교분석: 한국의 거시 경제 지표에의 응용

  • Received : 2014.02.13
  • Accepted : 2014.04.05
  • Published : 2014.06.30

Abstract

We introduce the empirical model decomposition (EMD) to decompose a time series into a set of components in the time-frequency domain. By using EMD, we also extract cycle and trend components from major Korean macroeconomic indices and forecast the indices with the components combined. In order to evaluate their efficiencies, we investigate volatility, autocorrelation, persistence, Granger causality, nonstationarity, and forecasting performance. They are then compared with those by Hodrick-Prescott filter which is the most commonly used method.

본 논문에서는 시간-진동수 영역에서 시계열을 여러 구성 성분으로 분해하는 방법인 경험적모드분해법(Empirical Mode Decomposition)을 소개하고, 이를 이용하여 한국의 주요 거시 경제 지표를 대상으로 순환변동과 추세 성분을 추출하고 예측에 활용한다. 그 효율성을 살펴보기 위하여, 추출된 구성 성분들의 변동성, 동행성, 지속성, 인과성, 비정상성 및 예측력을 계산하고, 가장 보편적으로 널리 사용되고 있는 Hodrick-Prescott 필터에 의한 결과와 비교한다.

Keywords

References

  1. Boashash, B. (1992). Estimating and interpreting the instantaneous frequency of a signal - part 1: fundamentals, Proceedings of the IEEE, 80, 519-538.
  2. Cho, S. and Son, Y. (2009). Time Series Analysis by Using SAS/ETS, Yulgok Books, Seoul.
  3. Hamilton, J. D. (1994). Time Series Analysis, Princeton University Press, Princeton.
  4. Hodrick, R. J. and Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation, Journal of Money, Credit, and Banking, 29, 1-16. https://doi.org/10.2307/2953682
  5. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H. (1998). The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis, Proceeding of the Royal Society London A, 454, 903-995. https://doi.org/10.1098/rspa.1998.0193
  6. Kim, D. and Oh, H.-S. (2009). EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46.
  7. Kim, D., Paek, S.-H. and Oh, H.-S. (2008). A Hilbert-Huang transform approach for predicting cyber-attacks, Journal of the Korean Statistical Society, 37, 277-283. https://doi.org/10.1016/j.jkss.2008.02.006
  8. Kim, D., Kim, K. and Oh, H.-S. (2012). Extending the scope of empirical mode decomposition by smoothing, EURASIP Journal on Advances in Signal Processing, 1, 168.
  9. Lee, J. (2009). Changes in the business cycle of the Korean economy: Evidence and explanations, KDI Journal of Economic Policy, 31, 47-85. https://doi.org/10.23895/kdijep.2009.31.2.47
  10. Mallat, S. (1998). A Wavelet Tour of Signal Processing, Academic press.
  11. Marcet, A. and Ravn, M. O. (2003). The HP-Filter in Cross-Country Comparisons, CEPR DP 4244.
  12. Nam, S.-H. (2007). An analysis on the characteristics of recent business cycles, Economic Analysis, 13, 79-109.
  13. Park, M., Kim, D. and Oh, H.-S. (2011). A reinterpretation of EMD by cubic spline interpolation, Advanced in Adaptive Data Analysis, 3, 527-540. https://doi.org/10.1142/S1793536911000921
  14. Rilling, G. and Flandrin, P. (2008). One or two frequencies? The empirical mode decomposition answers, IEEE Transactions on Signal Processing, 56, 85-95. https://doi.org/10.1109/TSP.2007.906771
  15. Ryu, M. and Lee, H. S. (2007). Measuring potential output and forecasting future inflation, Sogang Economic Papers, 36, 61-83.
  16. Tsay, R. S. (2005). Analysis of Financial Time Series, 2nd Edition, Wiley.

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