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| 模型置信集 (MCS)× | 逐步回归× | |
|---|---|---|
| 领域≠ | 计量经济学 | 统计学 |
| 方法族≠ | Hypothesis test | Regression model |
| 起源年份≠ | 2011 | 1960 |
| 提出者≠ | Hansen, Lunde & Nason | M. A. Efroymson |
| 类型≠ | Sequential hypothesis testing procedure for model comparison | Automated variable selection |
| 开创性文献≠ | Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. DOI ↗ | Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley. link ↗ |
| 别名≠ | MCS Procedure, Superior Set of Models, Model Selection Confidence Set, Model Güven Kümesi | stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection |
| 相关≠ | 3 | 5 |
| 摘要≠ | The Model Confidence Set (MCS) is a sequential hypothesis-testing procedure introduced by Hansen, Lunde, and Nason (2011) that identifies the smallest collection of forecasting or predictive models statistically indistinguishable from the best-performing model at a given confidence level. Instead of selecting a single winner, MCS returns a set of superior models, making it especially valuable in econometric forecast comparisons where the true best model is unknown. | Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library. |
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