Modelling underlying energy demand trends

Hunt, Lester C.; Judge, Guy; Ninomiya, Yasushi

doi:10.4337/9781781950364.00017

Cited by 24 publications

(32 citation statements)

References 16 publications

(22 reference statements)

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“…Harris (1995) To implement this technique, we start by modelling equation (1) as a conditional ARDL-ECM: where c 0 and c 1 t are the intercept and time trend components. Despite its restrictive nature, the latter is included to capture the effect of technical progress and other exogenous impacts that are not measurable directly (see Hunt et al, 2003, for further discussion). D t is a vector of dummy variables included to allow for significant trend or level breaks (due to shocks in prices or impacts related to the transition to independence) or pulses in the series (due to outlier observations).…”

Section: Methodsmentioning

confidence: 99%

An empirical analysis of energy demand in Namibia

2006

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Using a unique database of end-user local energy data and the recently developed Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration, we estimate the long-run elasticities of the Namibian energy demand function at both aggregated level and by type of energy (electricity, petrol and diesel) for the period 1980 to 2002. Our main results show that energy consumption responds positively to changes in GDP and negatively to changes in energy price and air temperature. The differences in price elasticities across fuels uncovered by this study have significant implications for energy taxation by Namibian policy makers. We do not find any significant cross-price elasticities between different fuel types.JEL Classification: Q41; Q42; Q48.

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Section: Methodsmentioning

confidence: 99%

An empirical analysis of energy demand in Namibia

2006

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“…Note that it has been argued that the UEDT should be stochastic in energy demand models (for example see [42,43]). However, given that the focus here is on using and comparing cointegration techniques the trend is constrained to be deterministic and although any stochastic trend is implicitly represented by the non-stationary variables in the cointegration analysis, it is not possible to identify explicitly the impact of an exogenous stochastic trend on electricity demand.…”

Section: Per Capita Electricity Demand Functionmentioning

confidence: 99%

Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques

2016

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Policymakers in developing and transitional economies require sound models to: (i) understand the drivers of rapidly growing energy consumption and (ii) produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios-as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis)-it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 4 1 2 % per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies). Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc.) and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 19 1 2 to 21 TWh.

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“…The results of the estimation are presented in Section 4, with a summary and conclusion in the final section. 9 As advocated by Hunt et al (2003a and2003b) 10 As proposed by Griffin and Schulman (2005) and Adeyemi and Hunt (2007). 11 The UEDT implicitly includes exogenous technical progress of the appliance and building stock and it could be argued that even though technologies are available to each state they are not necessarily installed at the same rate.…”

Section: Introductionmentioning

confidence: 99%