The Experts below are selected from a list of 4512 Experts worldwide ranked by ideXlab platform
Mike G. Tsionas - One of the best experts on this subject based on the ideXlab platform.
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when where and how of efficiency estimation improved procedures for Stochastic Frontier Modeling
Journal of the American Statistical Association, 2017Co-Authors: Mike G. TsionasAbstract:AbstractThe issues of functional form, distributions of the error components and endogeneity are for the most part still open in Stochastic Frontier models. The same is true when it comes to imposition of restrictions of monotonicity and curvature, making efficiency estimation an elusive goal. In this paper we attempt to consider these problems simultaneously and offer practical solutions to the problems raised by Stone (2002) and addressed in Badunenko, Henderson and Kumbhakar (2012). We provide major extensions to smoothly mixing regressions and fractional polynomial approximations for both the functional form of the Frontier and the structure of inefficiency. Endogeneity is handled, simultaneously, using copulas. We provide detailed computational experiments and an application to US banks. To explore the posteriors of the new models we rely heavily on Sequential Monte Carlo techniques.
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Recent Developments in Stochastic Frontier Modeling
2003Co-Authors: Subal C. Kumbhakar, Mike G. TsionasAbstract:Some of the recent developments in the efficiency measurement area using Stochastic Frontier models are: A. Estimation of the IO model, B. Latent class models to examine technological heterogeneity as well as heterogeneity in economic behavior, C. Estimation of Stochastic Frontier models using LML, D. Non-constant parameters: Random coefficient models with and without inefficiency, Markov switching Stochastic Frontier models, E. Estimation of cost/profit system with technical and allocative inefficiency using alternative techniques. We consider these as "open problems". In the past, estimation of some of these models was considered to be too difficult. Advances have been made in recent years to estimate some of these so-called difficult models. In this paper we will focus on the first three of the above topics. There are some papers that deal with issues listed under D and E. Both Bayesian and classical approaches are used to address these issues.
Subal C. Kumbhakar - One of the best experts on this subject based on the ideXlab platform.
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Crime in India: specification and estimation of violent crime index
Journal of Productivity Analysis, 2015Co-Authors: Kausik Chaudhuri, Payel Chowdhury, Subal C. KumbhakarAbstract:This paper addresses several important issues related to crime. First, we construct a violent crime index taking into account seven different types of crimes. We use an aggregator function to define a crime index that attaches crime-specific weights which can be interpreted as severity of each crime. These weights are estimated econometrically along with other parameters in the model thereby avoiding the problems associated with equally or arbitrary weighted aggregate crime index. Second, we utilize the aggregate crime index function to determine the impact of socio-economic variables on the overall (aggregated) crime, and further decompose them into crime-specific components. Third, in specifying the crime index we allow the possibility that crimes may be underreported and estimate crime underreporting using the Stochastic Frontier Modeling approach. We use district level data from India for the census years 1981, 1991 and 2001. Our results fail to support the equally weighted crime index model and provide evidence of substantial underreporting.
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Recent Developments in Stochastic Frontier Modeling
2003Co-Authors: Subal C. Kumbhakar, Mike G. TsionasAbstract:Some of the recent developments in the efficiency measurement area using Stochastic Frontier models are: A. Estimation of the IO model, B. Latent class models to examine technological heterogeneity as well as heterogeneity in economic behavior, C. Estimation of Stochastic Frontier models using LML, D. Non-constant parameters: Random coefficient models with and without inefficiency, Markov switching Stochastic Frontier models, E. Estimation of cost/profit system with technical and allocative inefficiency using alternative techniques. We consider these as "open problems". In the past, estimation of some of these models was considered to be too difficult. Advances have been made in recent years to estimate some of these so-called difficult models. In this paper we will focus on the first three of the above topics. There are some papers that deal with issues listed under D and E. Both Bayesian and classical approaches are used to address these issues.
Kien C Tran - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Frontier Approach of Measuring Agricultural Productivity and
2012Co-Authors: Kien C Tran, Er B. Darku, Stavroula MallaAbstract:It has been recognized that Modeling agricultural productivity growth and inefficiency is a challenging task since there exists many methodologies and each one has its own merits and drawbacks. The main purpose and motivation of this paper is to offer some new Modeling approaches to overcome the heterogeneity problem in measuring productivity and efficiency using Stochastic Frontier Modeling. As an extension, the paper also shows how to incorporate endogeneity of the input in the production process by mean of auxiliary information. Regarding the estimation, the paper proposed various procedures that are fairly easy to implement, given the current existing computing power and readily automated software packages. Finally, the paper suggests a way to decompose the TFP growth and efficiency measurements for policy formulation purposes
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on the choice of functional form in Stochastic Frontier Modeling
Empirical Economics, 2003Co-Authors: Konstantinos Giannakas, Kien C Tran, Vangelis TzouvelekasAbstract:This paper examines the effect of functional form specification on the estimation of technical efficiency using a panel data set of 125 olive-growing farms in Greece for the period 1987–93. The generalized quadratic Box-Cox transformation is used to test the relative performance of alternative, widely used, functional forms and to examine the effect of prior choice on final efficiency estimates. Other than the functional specifications nested within the Box-Cox transformation, the comparative analysis includes the minflex Laurent translog and generalized Leontief that possess desirable approximation properties. The results indicate that technical efficiency measures are very sensitive to the choice of functional specification. Perhaps most importantly, the choice of functional form affects the identification of the factors affecting individual performance – the sources of technical inefficiency. The analysis also shows that while specification searches do narrow down the set of feasible alternatives, the identification of the most appropriate functional specification might not always be (statistically) feasible.
Kausik Chaudhuri - One of the best experts on this subject based on the ideXlab platform.
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Crime in India: specification and estimation of violent crime index
Journal of Productivity Analysis, 2015Co-Authors: Kausik Chaudhuri, Payel Chowdhury, Subal C. KumbhakarAbstract:This paper addresses several important issues related to crime. First, we construct a violent crime index taking into account seven different types of crimes. We use an aggregator function to define a crime index that attaches crime-specific weights which can be interpreted as severity of each crime. These weights are estimated econometrically along with other parameters in the model thereby avoiding the problems associated with equally or arbitrary weighted aggregate crime index. Second, we utilize the aggregate crime index function to determine the impact of socio-economic variables on the overall (aggregated) crime, and further decompose them into crime-specific components. Third, in specifying the crime index we allow the possibility that crimes may be underreported and estimate crime underreporting using the Stochastic Frontier Modeling approach. We use district level data from India for the census years 1981, 1991 and 2001. Our results fail to support the equally weighted crime index model and provide evidence of substantial underreporting.
Payel Chowdhury - One of the best experts on this subject based on the ideXlab platform.
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Crime in India: specification and estimation of violent crime index
Journal of Productivity Analysis, 2015Co-Authors: Kausik Chaudhuri, Payel Chowdhury, Subal C. KumbhakarAbstract:This paper addresses several important issues related to crime. First, we construct a violent crime index taking into account seven different types of crimes. We use an aggregator function to define a crime index that attaches crime-specific weights which can be interpreted as severity of each crime. These weights are estimated econometrically along with other parameters in the model thereby avoiding the problems associated with equally or arbitrary weighted aggregate crime index. Second, we utilize the aggregate crime index function to determine the impact of socio-economic variables on the overall (aggregated) crime, and further decompose them into crime-specific components. Third, in specifying the crime index we allow the possibility that crimes may be underreported and estimate crime underreporting using the Stochastic Frontier Modeling approach. We use district level data from India for the census years 1981, 1991 and 2001. Our results fail to support the equally weighted crime index model and provide evidence of substantial underreporting.
