By Subhash C. Ray, Subal C. Kumbhakar, Pami Dua
This publication presents a close creation to the theoretical and methodological foundations of creation potency research utilizing benchmarking. of the extra renowned tools of potency overview are Stochastic Frontier research (SFA) and knowledge Envelopment research (DEA), either one of that are in line with the concept that of a construction threat set and its frontier. counting on the assumed pursuits of the decision-making unit, a construction, price, or revenue Frontier is created from saw info on enter and output amounts and costs. whereas SFA makes use of diversified greatest probability estimation concepts to estimate a parametric frontier, DEA is determined by mathematical programming to create a nonparametric frontier. yet one more substitute is the Convex Nonparametric Frontier, that's in accordance with the assumed convexity of the creation threat set and creates a piecewise linear frontier which includes a few tangent hyper planes.
Three of the papers during this quantity supply an in depth and comparatively effortless to stick to exposition of the underlying thought from neoclassical creation economics and supply step by step directions at the acceptable version to use in several contexts and the way to enforce them. Of specific attraction are the directions on (i) the way to write the codes for various SFA versions on STATA, (ii) the best way to write a VBA Macro for repetitive resolution of the DEA challenge for every construction unit on Excel Solver, and (iii) easy methods to write the codes for the Nonparametric Convex Frontier estimation. the 3 different papers within the quantity are essentially theoretical and may be of curiosity to PhD scholars and researchers hoping to make methodological and conceptual contributions to the sector of nonparametric potency analysis.
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Extra resources for Benchmarking for Performance Evaluation: A Production Frontier Approach
If ﬁner initial values are sought, the command sf_srch is useful, as shown in the following example. The command sf_srch is a wrapper of STATA’s ml_plot command that helps search for better initial values before starting the optimization process. The search is undertaken for one parameter at a time given other parameter values. 1 Estimation of Technical Inefﬁciency … 29 The option—n(#)—speciﬁes the number of times the search is to be performed on the speciﬁed parameters; thus,—n(2)—asks STATA to cycle through the parameters for a second time.
Xx). The example on p. xx shows how initial values may be supplied by the user using the sf_init command. It also shows the use of sf_srch to reﬁne the initial values. The following commands estimate a truncated normal model of Stevenson (1980). 1 Estimation of Technical Inefﬁciency … 43 The uses of sf_init and sf_srch are optional. If we wish to use results of the half-normal model as initial values, we may replace the second line by The following is the estimation result (the results are identical regardless which set of initial values is used or even, in this instance, if initial values are not provided, although convergence is much slower in this case).
The early literature adopts a two-step procedure to investigate the relationship. The approach estimates the observation-speciﬁc inefﬁciency index in the ﬁrst 1 Estimation of Technical Inefﬁciency … 37 step and then regresses the index on a vector of exogenous variables zi in the second step. , they are more efﬁcient). The two-step procedure, however, has long been recognized as biased because the model estimated in the ﬁrst step is misspeciﬁed. As explained in Wang and Schmidt (2002), if xi and zi are correlated, then the ﬁrst step of the two-step procedure is biased.