方法对比
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| Bootstrap DEA：效率得分的偏差校正与置信区间× | Malmquist生产率指数× | |
|---|---|---|
| 领域 | 效率分析 | 效率分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1998 | 1994 |
| 提出者≠ | Simar & Wilson | Färe, Grosskopf, Norris & Zhang |
| 类型≠ | Nonparametric efficiency estimation with bootstrap inference | Non-parametric productivity index |
| 开创性文献≠ | Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. DOI ↗ | Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83. link ↗ |
| 别名 | Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi |
| 相关≠ | 2 | 1 |
| 摘要≠ | Bootstrap Data Envelopment Analysis (Bootstrap DEA) is a resampling-based extension of standard DEA that provides statistically valid inference for efficiency scores. Introduced by Simar and Wilson in 1998, it addresses the core weakness of classical DEA — its inability to quantify uncertainty in estimated scores — by constructing bootstrap confidence intervals and bias-corrected efficiency estimates from repeatedly resampled pseudo-frontiers. | The Malmquist Productivity Index (MPI) is a non-parametric measure of total factor productivity (TFP) change over time. Formally grounded in distance functions by Caves, Christensen, and Diewert (1982) and operationalized using Data Envelopment Analysis by Färe, Grosskopf, Norris, and Zhang (1994), MPI decomposes productivity growth into two components: efficiency change (catching-up to the frontier) and technical change (shift of the frontier itself). |
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