Stochastic error specification in primal and dual production systems

Kumbhakar, Subal C.; Tsionas, Efthymios G.

doi:10.1002/jae.1100

Cited by 30 publications

(18 citation statements)

References 19 publications

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“…These data come from Kumbhakar and Tsionas (2011) , who use data 82 fossil-fuel-fired steam electric power-generating plants in the USA over the period 1986-1999 (case corresponds to n = 100 and t = 10 ). Here we estimate a cost function that is specified as a function of output (net steam electric power generation in megawatt-hours), prices of labor, fuel, capital, and time trend.…”

Section: Us Electric Utilities Datamentioning

confidence: 99%

When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models

Badunenko

Kumbhakar

2016

European Journal of Operational Research

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a b s t r a c tIn this paper we examine robustness of a recently developed panel data stochastic frontier model that allows for both persistent and transient (also known as long-run and short-run or time-invariant and time-varying) inefficiency along with random firm-effects (heterogeneity) and noise. We address some concerns that the practitioners might have about this model. First, given that there are two random time-invariant components (persistent inefficiency and firm-effects) the concern is whether the model can accurately identify them, and if so how precisely can the model estimate them? Second, there are two time-varying random components (transient inefficiency and noise), and the concern is whether the model can separate noise from transient inefficiency, and if so how precisely can the model estimate transient inefficiency? Third, how well are persistent and transient inefficiency estimated under different scenarios, viz., under different configurations of the variance parameters of the four random components? Given that the model is quite complex, relatively new and becoming quite popular in the panel efficiency literature, we feel that there is need for a detailed simulation study to examine when, where and how one can use this model with confidence to estimate persistent and transient inefficiency.

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Section: Us Electric Utilities Datamentioning

confidence: 99%

When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models

Badunenko

Kumbhakar

2016

European Journal of Operational Research

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“…There have been many papers since then along these same lines, but with different functional forms for the equation of interest and different assumptions about the behavior of the firm. Examples include Kumbhakar (1987), Atkinson and Cornwell (1994), Kumbhakar (1997), Kumbhakar and Tsionas (2005) and Kumbhakar and Tsionas (2009), to name just a few.…”

Section: Structural Approachesmentioning

confidence: 99%

Endogeneity in stochastic frontier models

Amsler

Prokhorov

Schmidt

2016

Journal of Econometrics

206

133

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Stochastic frontier models are typically estimated by maximum likelihood (MLE) orcorrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that there is correlation between the inputs and statistical noise or inefficiency.In a standard regression setting, simultaneity is handled by a number of procedures that are numerically or asymptotically equivalent. These include 2SLS; using the residual from the reduced form equations for the endogenous variables as a control function; and MLE of the system that contains the equation of interest plus the unrestricted reduced form equations for the endogenous variables (LIML). We will consider modifications of these standard procedures for the stochastic frontier setting.The paper is mostly a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature, but it also contains some new results. AbstractStochastic frontier models are typically estimated by maximum likelihood (MLE) or corrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that there is correlation between the inputs and statistical noise or inefficiency.In a standard regression setting, simultaneity is handled by a number of procedures that are numerically or asymptotically equivalent. These include 2SLS; using the residual from the reduced form equations for the endogenous variables as a control function; and MLE of the system that contains the equation of interest plus the unrestricted reduced form equations for the endogenous variables (LIML). We will consider modifications of these standard procedures for the stochastic frontier setting.The paper is mostly a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature, but it also contains some new results.

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“…The data set is obtained from Journal of Applied Econometrics data archive (http://www.econ.queensu.ca/jae/) and is described in Rungsuriyawiboon and Stefanou (2007) and Kumbhakar and Tsionas (2010). For the electricity industry, the output (electric power generated) is usually exogenously given (demand determined).…”

Section: Datamentioning

confidence: 99%

Estimation of TFP growth: a semiparametric smooth coefficient approach

Kumbhakar

Sun

2011

Empir Econ

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This paper uses a semiparametric smooth coefficient model (SPSCM) to estimate TFP growth and its components (scale and technical change). The SPSCM is derived from a nonparametric specification of the production technology represented by an input distance function, using a growth formulation. The functional coefficients of the SPSCM come naturally from the model and are fully flexible in the sense that no functional form of the underlying production technology is used to derive them. Another advantage of the SPSCM is that it can estimate bias (input and scale) in technical change in a fully flexible manner. We also used a translog input distance function framework to estimate TFP growth components. A panel of U.S. electricity generating plants for the period 1986 − 1998 is used for this purpose. Comparing estimated TFP growth results from both parametric and semiparametric models against the Divisia TFP growth, we conclude that the SPSCM performs the best in tracking the temporal behavior of TFP growth.

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Stochastic error specification in primal and dual production systems

Cited by 30 publications

References 19 publications

When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models

When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models

Endogeneity in stochastic frontier models

Estimation of TFP growth: a semiparametric smooth coefficient approach

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