Although conceptually pleasing, normal-gamma frontier models lead to difficult estimation problems. It is shown here that unless the sample size reaches several thousands of observations the shape parameter of the gamma density is hard to estimate, and that this carries over to estimates of the stochastic frontier, the individual inefficiencies, and the allocation of the overall variance to the stochastic frontier and to the inefficiencies
Stationary point results on the normal–half-normal stochastic frontier model are generalized using t...
Stochastic frontier models are one of the most frequently used approaches for estimating production ...
Estimation of the one sided error component in stochastic frontier models may erroneously attribute ...
Although conceptually pleasing, normal-gamma frontier models lead to difficult estimation problems. ...
The normal-gamma stochastic frontier model was proposed in Greene (1990) and Beckers and Hammond (19...
Stochastic frontier models all need an assumption on the distributional form of the (in)efficiency c...
The normal-gamma stochastic frontier model was proposed in Greene (1990) and Beckers and Hammond (19...
A stochastic frontier model is proposed in which the disturbances are a composite of additive gamma ...
The paper is concerned with several kinds of stochastic frontier models whose likelihood function is...
The presence of outliers in the data has implications for stochastic frontier analysis, and indeed a...
We consider the problem of estimating a stochastic frontier, i.e. a frontier that is subject to (add...
Stochastic frontier analysis (SFA) is extensively utilized to study production functions and to esti...
The stochastic frontier analysis (Aigner et al. [1] and Meeusen and van den Broeck [8]) has been wid...
Skewness is an intrinsic characteristic in Stochastic Frontier Analysis (SFA), where it is used as a...
The stochastic frontier model was first proposed in the context of production function estimation to...
Stationary point results on the normal–half-normal stochastic frontier model are generalized using t...
Stochastic frontier models are one of the most frequently used approaches for estimating production ...
Estimation of the one sided error component in stochastic frontier models may erroneously attribute ...
Although conceptually pleasing, normal-gamma frontier models lead to difficult estimation problems. ...
The normal-gamma stochastic frontier model was proposed in Greene (1990) and Beckers and Hammond (19...
Stochastic frontier models all need an assumption on the distributional form of the (in)efficiency c...
The normal-gamma stochastic frontier model was proposed in Greene (1990) and Beckers and Hammond (19...
A stochastic frontier model is proposed in which the disturbances are a composite of additive gamma ...
The paper is concerned with several kinds of stochastic frontier models whose likelihood function is...
The presence of outliers in the data has implications for stochastic frontier analysis, and indeed a...
We consider the problem of estimating a stochastic frontier, i.e. a frontier that is subject to (add...
Stochastic frontier analysis (SFA) is extensively utilized to study production functions and to esti...
The stochastic frontier analysis (Aigner et al. [1] and Meeusen and van den Broeck [8]) has been wid...
Skewness is an intrinsic characteristic in Stochastic Frontier Analysis (SFA), where it is used as a...
The stochastic frontier model was first proposed in the context of production function estimation to...
Stationary point results on the normal–half-normal stochastic frontier model are generalized using t...
Stochastic frontier models are one of the most frequently used approaches for estimating production ...
Estimation of the one sided error component in stochastic frontier models may erroneously attribute ...