Results 221 to 230 of about 9,803 (257)
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2000
Modern textbook presentations of production economics typically treat producers as successful optimizers. Conventional econometric practice has generally followed this paradigm, and least squares based regression techniques have been used to estimate production, cost, profit and other functions.
Kumbhakar, Subal C., Lovell, C.A. Knox
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Modern textbook presentations of production economics typically treat producers as successful optimizers. Conventional econometric practice has generally followed this paradigm, and least squares based regression techniques have been used to estimate production, cost, profit and other functions.
Kumbhakar, Subal C., Lovell, C.A. Knox
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Bounded rationality and thick frontiers in stochastic frontier analysis
European Journal of Operational Research, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Dynamic quantile stochastic frontier models
International Journal of Hospitality Management, 2020This paper introduces the concept of dynamic quantile regression to the context of stochastic frontier models. We develop a Dynamic Quantile Stochastic Frontier (DQSF) in a Bayesian framework to take into account possible shifts of production (i.e. outputs) over time.
Assaf, A. George +2 more
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Compression in stochastic frontier models
Annals of Tourism Research, 2021•We develop a compressed SF model to account for heterogeneity.•We allow for cross-sectional and time-series variation in all coefficients.•We use Bayesian Compression to reduce the dimensionality of the parameter space.
Mike G. Tsionas, A. George Assaf
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Stochastic Frontier Analysis: Foundations and Advances II
2020This chapter (as well as Chap. 11) reviews some of the most important developments in the econometric estimation of productivity and efficiency surrounding the stochastic frontier model.
Kumbhakar, Subal C. +2 more
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2018
Distance, revenue, cost and profit functions can always be written in the form of regression models with unobserved error terms representing statistical noise and different types of inefficiency. In practice, the noise components are almost always assumed to be random variables (i.e., stochastic).
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Distance, revenue, cost and profit functions can always be written in the form of regression models with unobserved error terms representing statistical noise and different types of inefficiency. In practice, the noise components are almost always assumed to be random variables (i.e., stochastic).
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2015
The stochastic frontier analysis is an econometric approach to efficiency measurement. The basic idea is the introduction of two error components, a random error term and an inefficiency term. For both terms, a distributional assumption is made, which facilitates maximum likelihood estimation.
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The stochastic frontier analysis is an econometric approach to efficiency measurement. The basic idea is the introduction of two error components, a random error term and an inefficiency term. For both terms, a distributional assumption is made, which facilitates maximum likelihood estimation.
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Spatial Stochastic Frontier Models
2010The stochastic frontier model with heterogeneous technical efficiency_x000D_ explained by exogenous variables is augmented with a sparse spatial autoregressive component for a cross-section data, and a spatial-temporal component for a panel data.
Josef Yap +2 more
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ปัจจัยกำหนดประสิทธิภาพของธนาคารพาณิชย์ไทยโดยแบบจำลอง STOCHASTIC FRONTIER
วิทยานิพนธ์ฉบับนี้มีจุดประสงค์ เพื่อศึกษาถึงความมีประสิทธิภาพของธนาคารพาณิชย์ไทย โดยในการศึกษาแบ่งเป็น 2 ส่วน คือ ศึกษาประสิทธิภาพของธนาคารพาณิชย์ในด้านการสร้างมูลค่า โดยใช้แนวคิดแบบ STOCHASTIC FRONTIER ANALYSIS ในส่วนที่สองคือการศึกษาปัจจัยกำหนดความมีประสิทธิภาพโดยประมาณการโดยแบบจำลอง FIXED EFFECT และใช้ข้อมูลของธนาคารพาณิชย์ไทยจำนวน 13 ธนาคารระหว่างปีopenaire +1 more source
Stochastic Frontier Analysis SFA
2010As explained in Chap. 1, there are two dominant approaches to modern benchmarking. One is the non-parametric, deterministic DEA approach discussed in some detail in the last three chapters; other is the stochastic frontier analysis (SFA), which we will cover in this and the next chapter.
Peter Bogetoft, Lars Otto
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