Time Varying Parameters Bayesian Forecasting of Electricity Demand: The Italian Case

IEFE Working Paper No. 36

30 Pages Posted: 26 Jul 2010

See all articles by Margherita Grasso

Margherita Grasso

University College London - Department of Economics

Date Written: July 26, 2010

Abstract

Electricity demand is modeled as a time-varying parameters (TVP) vector autoegression with or without imposing cointegration. The paper applies Bayesian strategies where all or a part of the parameters are allowed to vary, and compares their forecasts performances with alternative time series models, namely a seasonal ARIMA (SARIMA) specification and a vector error correction model (VECM). Considering Italian data, the appropriate diagnostic tests and estimation results are in favour of non-stability of the parameters. However, the forecasts abilities of the models do not show significant differencies when measured by RMSE and MAE, and compared trough the Diebold Mariano statistic. On the other hand, forecast intervals of Bayesian models show higher empirical coverage rates.

Keywords: Electricity demand, forecasting, time varying coefficients model, Kalman filtering, Markov Chain Monte Carlo

JEL Classification: C11, C52, C53, Q47

Suggested Citation

Grasso, Margherita, Time Varying Parameters Bayesian Forecasting of Electricity Demand: The Italian Case (July 26, 2010). IEFE Working Paper No. 36. Available at SSRN: https://ssrn.com/abstract=1648786 or http://dx.doi.org/10.2139/ssrn.1648786

Margherita Grasso (Contact Author)

University College London - Department of Economics ( email )

Gower Street
London
United Kingdom

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