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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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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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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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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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A stochastic frontier regression model with dynamic frontier
Communications in Statistics - Simulation and Computation, 2020We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence.
T. V. Ramanathan +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.
openaire +1 more source