Salīdzināt metodes

	Bootstrap DEA: Bias Correction and Confidence Intervals for Efficiency Scores	Malmkvista produktivitātes indekss
Nozare	Efektivitātes analīze	Efektivitātes analīze
Saime	Regression model	Regression model
Izcelsmes gads	1998	1994
Autors	Simar & Wilson	Färe, Grosskopf, Norris & Zhang
Tips	Nonparametric efficiency estimation with bootstrap inference	Non-parametric productivity index
Pirmavots	Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.	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.
Citi nosaukumi	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	MPI, Malmquist Index, Malmquist DEA Productivity Index, Malmquist Verimlilik Endeksi
Saistītās	2	1
Kopsavilkums	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).