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| 호드릭-프레스콧 필터: 거시경제 시계열의 추세-경기 변동 분해× | STL 분해: Loess를 이용한 계절-추세 분해× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1997 | 1990 |
| 창시자≠ | Robert Hodrick & Edward Prescott | Cleveland, Cleveland, McRae & Terpenning |
| 유형≠ | Penalized least-squares smoother | nonparametric iterative smoother |
| 원전≠ | 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 ↗ |
| 별칭 | 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) |
| 관련 | 3 | 3 |
| 요약≠ | 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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