方法对比
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| Hodrick-Prescott 滤波器：宏观经济时间序列的趋势-周期分解× | STL分解：使用Loess的季节-趋势分解× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1997 | 1990 |
| 提出者≠ | Robert Hodrick & Edward Prescott | Cleveland, Cleveland, McRae & Terpenning |
| 类型≠ | Penalized least-squares smoother | nonparametric iterative smoother |
| 开创性文献≠ | Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16. DOI ↗ | Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73. link ↗ |
| 别名 | Hodrick-Prescott Filter, HP Decomposition, Trend-Cycle Filter, HP Filtresi | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| 相关 | 3 | 3 |
| 摘要≠ | The Hodrick-Prescott (HP) filter is a penalized least-squares technique used in macroeconomics and empirical finance to decompose a time series into a smooth long-run trend component and a short-run cyclical component. Introduced by Hodrick and Prescott (1997) using postwar U.S. business cycle data, it has become one of the most widely applied filters in business cycle analysis, monetary policy research, and applied econometrics. | STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition methods. |
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