Erkie Asmare 1 and Andualem Begashaw

Methods and Procedures for Parametric and 
Nonparametric Methods 
The parametric approach
Parametric efficiency analysis is one of the most investigated 
area sin applied production economics [5]. The measurement of 
productive efficiency by means of parametric techniques requires 
the specification of a particular frontier function [4,10]. In addition, 
parametric statistical inference is generally based on the assump-
tion that the random samples are selected from some underlying 
(e.g., normal) distribution [17]. Stochastic frontiers are commonly 
used in parametric methods and estimated by the Maximum Likeli-
hood (ML) estimator [14]. 
Therefore, parametric models postulate a priori a specific func
-
tional form (e.g., Cobb-Douglas, translog, etc.) and subsequently es
-
timate its unknown parameters [1]. The stochastic frontier models 
capture the effects of exogenous shocks beyond the control of the 

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How to cite this article:
Erkie A, Andualem B. Review on Parametric and Nonparametric Methods of Efficiency Analysis. Open Acc Biostat Bioinform
. 2(2). 
OABB.000534. 2018. DOI:
10.31031/OABB.2018.02.000534
Open Acc Biostat Bioinform
Copyright © 
Erkie Asmare Beyene
Volume 2 - Issue - 2
analyzed units. Errors in the observations and in the measurement 
of output are also taken into account in this kind of models [7]. 
Parametric statistical inference is generally based on the assump-
tion that the random samples are selected from some underlying 
(e.g., normal) distribution [17]. Therefore, most of parametric sta
-
tistical tests are reliable even if there are slight departures from 
normality, as long as sample sizes are large [17].