In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production efficiency. Specifically, our approach first allows for statistical noise, similar to STOCHASTIC FRONTIER ANALYSIS, and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods. The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Kumbhakar et al. We will use local-spherical coordinate system to transform multi-input multi-output data to more flexible system which we can use in our approach. We also illustrate the performance of our approach with simulated example.

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) was evaluated, with some determinants of technical efficiency identified, in Bangladesh. It was attempted, throughout this study, to access the status of technical efficiency in rice production in Bangladesh for panel data while using the STOCHASTIC FRONTIER Production Model with either of truncated normal or half-normal distributional assumptions. Both time-variant and time-invariant inefficiency effects models were estimated, one at a time. Collected data from agricultural sector pertaining to three main rice crops in Bangladesh for the period of 1980 to 2008 were made used of throughout the study. The results revealed that technical efficiency gradually increased over the reference period with the half normal distribution being found preferable to the truncated normal distribution as regards the technical inefficiency effects. The value of technical efficiency was found high for Boro rice while low for Aus in comparison with Aman rice in Bangladesh for both distributions in either of time-variant or invariant ones. It was observed that the most efficient rice production system has occurred for the case of Boro with a technical efficiency of 0.98. Year wise mean technical efficiency increased during the reference time periods.

Most research on performance evaluation of agricultural crops in Iran analyzes technical efficiency with estimation of STOCHASTIC production function. In this paper in spite of past research we survey cost efficiency with panel data. Information about input prices, yield and production cost per hectare collected for 28 provinces in 10 years and STOCHASTIC FRONTIER cost function estimated with panel data in two methods, parametric STOCHASTIC FRONTIER and partially non-parametric STOCHASTIC FRONTIER. The results show that according to parameter significance and discretion of production structure parametric methodology is more suitable than non- parametric methodology.Land rent (price) had maximum influence and chemical fertilizer price had minimum influence on FRONTIER production cost per hectare. Cost efficiency of wheat production in Iran is suitable and over 90 percent. Surveyed period Khuzestan province had maximum cost efficiency and Yazd province had minimum cost efficiency in wheat production. In whatever province where efficiency is low, agricultural education and knowledge needed to improve further.

The main purpose of this paper is to analyze the technical efficiency of knowledge firms as a knowledge producer. First, the knowledge production process is modeled using a STOCHASTIC FRONTIER of knowledge production. Then, the model is tested using knowledge firm data in Iran. The empirical results indicate that production process in knowledge firm includes two main stages: research and knowledge conversion. In research stage, the knowledge firm uses knowledge worker, knowledge capital and scientific equipments to produce new explicit knowledge. In conversion stage, it uses new explicit knowledge and tacit knowledge to produce knowledge products. In these stages, firm characteristics such as size, ownership, research collaboration with other organizations have influence an efficiency of knowledge firms.

Air transport is an important component of the transportation industry of any country and airports are considered as one of the key inputs of this industry. Therefore, it is crucial to assess the performance and efficiency of airports and take appropriate measures to increase their efficiencies. To that end, in the present study we use STOCHASTIC FRONTIER ANALYSIS approach to examine the technical efficiency of selected international airports in Iran (i. e. Mehrabad, Mashhad, Shiraz, Tabriz, and Isfahan airports). The findings using the data for the period 2010-2015 show that 98% of the inefficiencies observed in these airports are due to the technical inefficiencies. In addition, none of the airports under study had achieved the complete efficiency and the lowest efficiency is observed in 2015. The results also indicate that Mashhad and Tabriz airports ranked first, with the highest efficiency among the five selected airports. Isfahan and Mehrabad airports ranked second and last, respectively.

Nowadays, the most important factor affecting production is efficiency, which recovers economic performance, adjusts prices, enhances competitiveness, and improves social welfare and growth sustainability by influencing the production level. This paper aims to estimate the technical efficiency of production in Iran's industrial sector by implementing STOCHASTIC FRONTIER ANALYSIS (SAF); To this end, the Translog Function is used to estimate the production and technical performance of the Iranian industry sector in terms of four-digit International Standard Industrial Classification (ISIC) codes for the period 2010-2018. Accordingly, the panel data technique is applied to estimate the technical efficiency and production function FRONTIER Production Functions, the estimator of maximum likelihood variables of using value-added variables, labor force, capital, and energy based on four-digit ISIC codes. The results show that the chemical, petroleum, and coal industries ranked first in efficiency; textile manufacturing, clothing industry, wood, and cork industry, manufacture of articles of straw and plaiting materials, and paper and paper products ranked second in efficiency; Coke production and refined petroleum products, chemicals and chemical products, and manufacturing other non-metallic mineral products ranked third in efficiency; and lastly, manufacturing basic metals and fabricated metal products, except machinery and equipment, ranked as fourth.