By Subhash C. Ray, Subal C. Kumbhakar, Pami Dua
This e-book offers a close advent to the theoretical and methodological foundations of creation potency research utilizing benchmarking. of the extra well known equipment of potency evaluate are Stochastic Frontier research (SFA) and knowledge Envelopment research (DEA), either one of that are according to the concept that of a construction chance set and its frontier. reckoning on the assumed targets of the decision-making unit, a construction, expense, or revenue Frontier is created from saw information on enter and output amounts and costs. whereas SFA makes use of varied greatest probability estimation ideas to estimate a parametric frontier, DEA depends on mathematical programming to create a nonparametric frontier. another replacement is the Convex Nonparametric Frontier, that is in accordance with the assumed convexity of the construction danger set and creates a piecewise linear frontier such as 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 idea from neoclassical construction economics and provide step by step directions at the acceptable version to use in several contexts and the way to enforce them. Of specific charm are the directions on (i) the way to write the codes for various SFA types on STATA, (ii) easy methods to write a VBA Macro for repetitive answer of the DEA challenge for every creation unit on Excel Solver, and (iii) tips 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 sphere of nonparametric potency analysis.
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Extra resources for Benchmarking for Performance Evaluation: A Production Frontier Approach
2), the truncated normal distribution with scaling properties (Sect. 3), and the 1 Estimation of Technical Inefﬁciency … 21 exponential distribution (Sect. 3 Readers are encouraged to read through the next section (on the half-normal distribution model) even if their interests lie in other models, because many of the discussions there are pertinent to the later sections but are not repeated. 5), a stochastic production frontier model with a normal distribution on vi and a half-normal distribution on ui can be written as ln yi ¼ ln yÃi À ui ; ð1:18Þ ln yÃi ¼ xi b þ vi ; ð1:19Þ ui $ N þ ð0; r2 Þ; ð1:20Þ vi $ Nð0; r2v Þ; ð1:21Þ where vi and ui are independently distributed to each other.
As explained in Wang and Schmidt (2002), if xi and zi are correlated, then the ﬁrst step of the two-step procedure is biased. Even when xi and zi are uncorrelated, ignoring the dependence of inefﬁciency on zi will cause the ﬁrst-step technical efﬁciency index to be underdispersed, so that the results of the second-step regression are likely to be biased downward. Wang and Schmidt provide Monte Carlo evidence of the bias. Given the undesirable statistical properties of the two-step procedure, the preferred approach to studying the exogenous influences on efﬁciency is the single-step procedure.
Mðui jei Þ ¼ 0). In contrast, none of the observations will be fully efﬁcient if one uses the conditional mean estimator ðEðui jei ÞÞ. Consequently, average inefﬁciency for a sample of ﬁrms will be lower if one uses the modal estimator. Since the conditional distribution of u is known, one can derive moments of any continuous function of uje. That is, we can use the same technique to obtain observation-speciﬁc estimates of the efﬁciency index ðexpðÀui ÞÞ. 27). Maximum likelihood estimates of the parameters are substituted into the equation to obtain the empirical values.
Benchmarking for Performance Evaluation: A Production Frontier Approach by Subhash C. Ray, Subal C. Kumbhakar, Pami Dua