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Massimo Filipini (Centre for Energy Policy and Economics, ETH Zurich, Suiza): "USA Total Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach".

09/01/2013

Miércoles 9 de Enero (10,15 h)

ABSTRACT This paper estimates a ‘frontier' total aggregate energy demand function (aggregate input demand function) using panel data for 49 US states over the period 1995 to 2009 using stochastic frontier analysis (SFA). Utilising an econometric energy demand model, the (in)efficiency of each state is modelled and it is argued that this represents a measure of the inefficient use of energy in each state (i.e. ‘waste energy'). This underlying efficiency is therefore observed for each state over time as well as the relative efficiency across the 49 USA states. Moreover, the analysis suggests that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controlling for a range of economic and other factors, the measure of energy efficiency obtained via this approach is. This is an approach to model energy demand and efficiency based on previous work by Filippini and Hunt (2011,2012) and it is arguably particularly relevant, given current USA energy policy discussions related to energy efficiency. From the econometric point of view, the aggregate energy demand function is estimated using the following models for panel data: the pooled model (PM hereafter); the random effects model proposed by Pitt and Lee (1981) (REM hereafter); the true fixed effects model (TFEM hereafter); true random effects model (TREM hereafter). Moreover, as shown by Farsi et. Al. (2005) and by Filippini and Hunt (2012) it is also possible to estimate some of these models using a Mundlak adjustment in order to take into account the econometric problem of unobserved heterogeneity bias (MPM and MRPM). The choice of the right model is not straightforward and depends, of course, on the goal and type of data and variables that are available.