We consider goodness–of–fit tests for the distribution of composed error in Stochastic Frontier Models. The proposed test statistic utilizes characteristic function term, and is formulated as a weighted integral properly standardized data. new shown to be consistent computationally convenient. Simulation results are presented whereby resampling versions compared classical methods.

By using a stochastic frontier framework, the mutual effect of input use on production risk and inefficiency is investigated. Disentangling this mutual effect proves important for empirical reasons, at least when applied to west Tennessee cotton systems grown after various cover crops. The most striking result is that the stochastic frontier model, when compared with a typical Just-Pope model, ...

Finite mixture estimation (FME) is compared to estimated generalized least squares (EGLS) in the estimation of economies of size and production cost frontiers for Alabama dairy farms. FME provides several unique insights into the economic forces behind recent changes in Alabama’s dairy industry. FME provides estimation of a stochastic average cost frontier with known statisticalproperties, whic...

Parametric stochastic frontier models have a long history in applied production economics, but the class of tractible parametric models is relatively small. Consequently, researchers have recently considered non–parametric alternatives such as kernel density estimators, functional approximations, and data envelopment analysis (DEA). The purpose of this paper is to present an information theoret...

Literature of productive efficiency analysis is currently divided between two main paradigms: the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper develops a new encompassing framework that melds the SFA-style stochastic composite error term to the DEA-type nonparametric frontier that satisfies monotonicity and concav...

The paper proposes to combine stochastic frontier models and linear programming methods by using DEA measures as priors of efficiency in the stochastic frontier model. These prior measures are revised to obtain posterior measures using Bayes theorem. Monte Carlo methods are developed to perform empirical Bayes inference in the new model. The methods are organized around Gibbs sampling with data...

A lot is known about the Hölder regularity of stochastic processes, in particular in the case of Gaussian processes. Recently, a finer analysis of the local regularity of functions, termed 2-microlocal analysis, has been introduced in a de-terministic frame: through the computation of the so-called 2-microlocal frontier, it allows in particular to predict the evolution of regularity under the a...

efficiency in agricultural production is indicative of the efficiency level of farm households in their farming activities. farmers in developing countries do not make use of all the potential technological resources, thus making inefficient decisions in their agricultural activities. herein, technical efficiency in relation with the production of three types of rice crop (boro, aus and aman) w...

A stochastic frontier production function is defined for panel data on firms, in which the non-negative technical inetGciency effects are assumed to be a function of firm-specific variables and time. The inefficiency effects are assumed to be independently distributed as truncations of normal distributions with constant variance, but with means which are a linear function of observable variable...