Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling

Virginie Dordonnat, Siem Jan Koopman and Marius Ooms

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3134-3152

Abstract: A dynamic multivariate periodic regression model for hourly data is considered. The dependent hourly univariate time series is represented as a daily multivariate time series model with 24 regression equations. The regression coefficients differ across equations (or hours) and vary stochastically over days. Since an unrestricted model contains many unknown parameters, an effective methodology is developed within the state–space framework that imposes common dynamic factors for the parameters that drive the dynamics across different equations. The factor model approach leads to more precise estimates of the coefficients. A simulation study for a basic version of the model illustrates the increased precision against a set of univariate benchmark models. The empirical study is for a long time series of French national hourly electricity loads with weather variables and calendar variables as regressors. The empirical results are discussed from both a signal extraction and a forecasting standpoint.

Keywords: Kalman filter; Electricity load forecasting; Forecasting; Periodic regression; Signal extraction; Time-varying parameter (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3134-3152

DOI: 10.1016/j.csda.2011.04.002

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