Phép lọc tuyến tính và vấn đề khử xu hướng của chuỗi thời gian: Nghiên cứu thực nghiệm tại Việt Nam
DOI:
https://doi.org/10.24311/jabes/2022.33.05.02Keywords:
Baxter-King filter, Hodrick-Prescott filter, Linear filtersAbstract
Filters are the popular tools for analyzing a time series into trend and cyclical components. The common property of the filters is retaining some components of the original time series as well as affecting the amplitude and the phase of the series received after filtering. The article analyzes the characteristic properties of popular linear filters: Differences, moving averages, high-pass, low-pass, band-pass, Hodrick-Prescott and Baxter-King filters through analysis of the transfer functions and the gain functions. For the purpose of extracting the trend component, only high-pass (HF), Hodrick-Prescott (HPF) and Baxter-King (BKF) filters can be used. Besides, these three types of filters do not change the phase and the amplitude of the cyclical component compared to the original data series. With the experimental study of HF, HPF, BKF filters on the weekly frequency
VN-Index, the authors find out the reasonable parameters for HF, HPF, and BKF.
References
Baxter, M., & King, R. G. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics, 81(4), 575–593.
Dadashova, B. (2012). Detrending the business cycles: Hodrick-Prescott and Baxter-King filters, 1–23. Universidad Carlos III de Madrid.
Hamilton, J. D. (2020). Time Series Analysis. United States: Princeton University Press.
Hamilton, J. D. (2018). Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics, 100(5), 831–843. doi: 10.1162/rest_a_00706
Hodrick, R., & Prescott, E. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16. doi: 10.2307/2953682
Hornsterin, A. (1998). Inventory investment and the business cycle. Federal Reserve Bank Richmond Economic Quarterly, 84(2), 47–71.
Iacobucci, A. (2005). Spectral analysis for economic time series. In Leskow J., Punzo L. F., Anyul M. P. (eds), Lecture Notes in Economics and Mathematical Systems, 551, 203–219. Berlin, Heidelberg: Springer. doi: 10.1007/3-540-28444-3_12
Kamalian, A., Zamani, Z., Amirali, M., & Dehkordi, M. M. (2020). Analyzing the different effects of endogenous and exogenous money supply on inflation: A spectral analysis approach. The Economic Research, 20(3), 57–77. Retrieved from https://ecor.modares.ac.ir/article-18-37417-en.html
Neusser, K. (2016). Time Series Econometrics. Berlin, Heidelberg: Springer.
Ravn, M. O., & Uhlig, H. (2002). On adjusting the Hodrick-Prescott filter for the frequency of observations. Review of Economics and Statistics, 84(2), 371–376. doi: 10.1162/003465302317411604
Tastan, H., & Sahin, S. (2020). Low-frequency relationship between money growth and inflation in Turkey. Quantitative Finance and Economics, 4(1), 91–120. doi: 10.3934/qfe.2020005
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