Econometrics of Panel Data Estimation

  • Nabaz T. KhayyatEmail author
Part of the Green Energy and Technology book series (GREEN)


An issue not to be ignored in econometric modeling of production technology and firm behavior is the heterogeneity with respect to production technology and productivity, and heterogeneity with respect to input demand. Industries that use the same amount of input often experience different levels of output. The assumption of homogeneous firms in the neoclassical production theory may not be suitable for many industries. The heterogeneity should be accounted for in empirical studies with econometric modeling. The availability of panel data set makes it easy to use econometric panel data techniques to account for heterogeneity. The producer heterogeneity under risk can operate on several stages: The production process, the risk preferences, and the expectation formation with respect to price and output. However, only heterogeneity with respect to the production process is relevant for estimating production function. A two-stage approach is used to model industrial demand for energy. In the first stage, a model to determine the total demand for energy as a derived input factor of production is specified and estimated. Here, the demand for energy is considered as a dependent variable, and then a Translog production function model incorporating non-ICT capital, labor, and energy as input factors of production is estimated. Furthermore, elasticities of substitution are calculated. In this study three specifications of mean function of the risk model are specified and compared: A general production function where energy is an input, a Translog energy demand function where energy is a dependent variable, and a Translog energy demand model generalized to incorporate risk function.


Energy Demand Function Panel dataPanel Data Produce Mapping Populations Industry-specific Effects Translog Flexible Functional Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Technology Management, Economics, and Policy Program, College of EngineeringSeoul National UniversitySeoulSouth Korea

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