方法对比
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| LOESS / LOWESS局部回归× | X-13ARIMA-SEATS 季节调整× | |
|---|---|---|
| 领域≠ | 机器学习 | 计量经济学 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 1979 | 1998 |
| 提出者≠ | William S. Cleveland | U.S. Census Bureau; Findley et al. |
| 类型≠ | Local nonparametric regression smoother | Non-parametric / model-based hybrid |
| 开创性文献≠ | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ | 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 ↗ |
| 别名 | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon | X-13ARIMA-SEATS, X-12-ARIMA, Census X-13, Mevsimsel Düzeltme X-13 |
| 相关 | 3 | 3 |
| 摘要≠ | LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots. | 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. |
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