方法对比
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| X-13ARIMA-SEATS 季节调整× | 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≠ | 1998 | 1990 |
| 提出者≠ | U.S. Census Bureau; Findley et al. | Cleveland, Cleveland, McRae & Terpenning |
| 类型≠ | Non-parametric / model-based hybrid | nonparametric iterative smoother |
| 开创性文献≠ | Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C., & Chen, B.-C. (1998). New capabilities and methods of the X-12-ARIMA seasonal adjustment program. Journal of Business & Economic Statistics, 16(2), 127–152. 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 | X-13ARIMA-SEATS, X-12-ARIMA, Census X-13, Mevsimsel Düzeltme X-13 | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| 相关 | 3 | 3 |
| 摘要≠ | X-13ARIMA-SEATS is the standard seasonal adjustment program produced by the U.S. Census Bureau, combining RegARIMA pre-adjustment with either the classical X-11 filter or the model-based SEATS signal-extraction algorithm. It is the official tool used by national statistical agencies worldwide — including Eurostat and the U.S. Bureau of Labor Statistics — to remove recurring calendar and seasonal patterns from monthly or quarterly economic time series such as GDP, employment, and retail sales. | 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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