The Energy Journal · 2003 · 72 citations · 40 references
Social SciencesBusiness EconomicsEconomicsDemand ForecastingEconometricsForecastingAggregate Demand ForecastingEnergy Demand ForecastingForecasting MethodsTime Series EconometricsTime Series ForecastingTime Series ModelingAggregate Demand AnalysisAutoregressive Distributed Lag ModelsAutoregressive ModelsDistributed Lag ModelsEconometric Demand ModelsElectric Load ForecastingElectricity Consumption ForecastingError Correction In Forecasting
Models of energy demand in New Zealand have typically been based upon either a partial general equilibrium approach or constructed from spreadsheet models. The results created by such methods predict that electricity is forecast to be the fastest growing energy demanded by households and the industrial sectorfor the next two decades. Furthermore, aggregate electricity demand is forecast to grow at a constant rate for the next two decades. In this paper we attempt to model and forecast electricity demand using a number of recent econometn'c approaches including Engle-Granger’s Error Correction Model, Phillip and Hansen’s (1990) Fully Modified Least Squares, and the AutoRegressive Distributed Lag (ARDL) approach of Pesaran et al. (1996, 1998). We identify the model with the smallest forecasting error using a series offorecasting measures and conclude that the new ARDL approach of Pesaran et al, has better forecasting performance than the other approaches considered.
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Testing for a unit root in time series regression
Peter C.B. Phillips, Pierre Perrón · Biometrika · 1988 · 17.9K citations
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Søren Johansen · Journal of Economic Dynamics and Control · 1988 · 16.8K citations
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