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| 부트스트랩 DEA: 효율성 점수에 대한 편향 보정 및 신뢰 구간× | 말퀴스트 생산성 지수(Malmquist Productivity Index)× | |
|---|---|---|
| 분야 | 효율성 분석 | 효율성 분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1998 | 1994 |
| 창시자≠ | Simar & Wilson | Färe, Grosskopf, Norris & Zhang |
| 유형≠ | Nonparametric efficiency estimation with bootstrap inference | Non-parametric productivity index |
| 원전≠ | 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 ↗ |
| 별칭 | Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi | MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi |
| 관련≠ | 2 | 1 |
| 요약≠ | 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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