By Subhash C. Ray, Subal C. Kumbhakar, Pami Dua
This ebook presents an in depth advent to the theoretical and methodological foundations of construction potency research utilizing benchmarking. of the extra well known tools of potency overview are Stochastic Frontier research (SFA) and knowledge Envelopment research (DEA), either one of that are in response to the idea that of a construction threat set and its frontier. counting on the assumed targets of the decision-making unit, a creation, expense, or revenue Frontier is made from saw information on enter and output amounts and costs. whereas SFA makes use of assorted greatest probability estimation suggestions to estimate a parametric frontier, DEA is determined by mathematical programming to create a nonparametric frontier. another replacement is the Convex Nonparametric Frontier, that is in response to the assumed convexity of the construction risk set and creates a piecewise linear frontier together with a couple of tangent hyper planes.
Three of the papers during this quantity supply a close and comparatively effortless to persist with exposition of the underlying thought from neoclassical construction economics and supply step by step directions at the applicable version to use in numerous contexts and the way to enforce them. Of specific charm are the directions on (i) tips on how to write the codes for various SFA types on STATA, (ii) the best way to write a VBA Macro for repetitive answer 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 basically theoretical and should 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 info for Benchmarking for Performance Evaluation: A Production Frontier Approach
Note that the equation also implies sign @ðui Þ ¼ sign(w½kÞ: @z½k ð1:42Þ Therefore, the sign of the coefﬁcient reveals the direction of impact of zi on (ui). So if we do not compute the exact marginal effect, we may still say something about the direction of the impact by the sign of the coefﬁcient. This is a convenient property, but the property does not always hold in models with a more complicated setup. Example Model 5: half-normal model with heterogeneity In this example, we estimate a half-normal model with an exogenous determinant of inefﬁciency (IT expenditure as a percentage of total expenditure, comp).
Therefore, by parameterizing r2 , we allow the effect of zi to affect the expected value of inefﬁciency. Marginal effects of the exogenous determinants If the effects of the exogenous factors on efﬁciency are the key concern, the maximum likelihood estimates of wu in Eq. 38) may not be very informative. This is because the relationship between Eðui Þ and zu is nonlinear, and so the slope coefﬁcients of wu are not the marginal effects of zu. 5. This number itself tells us nothing about the magnitude of the kth variable’s (marginal) effect on the inefﬁciency.
1982), is to estimate the expected value of ui conditional on the composed error of the model: ei vi À ui . This measure is known as the conditional mean of ui. The composed error contains individual-speciﬁc information, and so the conditional expectation yields observation-speciﬁc value of the inefﬁciency. This is like extracting signal from noise. Jondrow et al. C. -J. 27). Maximum likelihood estimates of the parameters are substituted into the equation to obtain the empirical estimate of the inefﬁciency index.