Showing 1 - 10 of 104
Many seasonal macroeconomic time series are subject to changes in their means and variances over a long time horizon. In this paper we propose a general treatment for the modelling of time-varying features in economic time series. We show that time series models with mean and variance functions...
Persistent link: https://www.econbiz.de/10008838615
We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...
Persistent link: https://www.econbiz.de/10008838634
This paper discusses identification, specification, estimation and forecasting for a general class of periodic … formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues …
Persistent link: https://www.econbiz.de/10005137026
for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … the intraday volatility measure. For forecasting horizons ranging from one day to one week the most accurate out …
Persistent link: https://www.econbiz.de/10005136957
We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are … implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework and is applied to … national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 AM, but forecasting results are …
Persistent link: https://www.econbiz.de/10005144435
-sample fit and out-of-sample forecasting. We also demonstrate how our model can be used to decompose own and cross price …
Persistent link: https://www.econbiz.de/10005137001
We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach provides a unified and consistent framework for introducing...
Persistent link: https://www.econbiz.de/10005209514
Novel periodic extensions of dynamic long memory regression models with autoregressive conditional heteroskedastic errors are considered for the analysis of daily electricity spot prices. The parameters of the model with mean and variance specifications are estimated simultaneously by the method...
Persistent link: https://www.econbiz.de/10005144404
We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the...
Persistent link: https://www.econbiz.de/10009386532
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...
Persistent link: https://www.econbiz.de/10008838568