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An hourly periodic state space model for modelling French national electricity load

Dordonnat, V.; Koopman, Siem Jan; Ooms, Marius; … - 2008

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...

Persistent link: https://www.econbiz.de/10011373810

Pseudo-variance quasi-maximum likelihood estimation of semiparametric time series models

Armillotta, Mirko; Gorgi, Paolo - 2023

We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional …

Persistent link: https://www.econbiz.de/10014380737

A multivariate asymmetric long memory conditional volatility model with X, regularity and asymptotics

Asai, Manabu; McAleer, Michael - 2016

The paper derives a Multivariate Asymmetric Long Memory conditional volatility model with Exogenous Variables (X), or the MALMX model, with dynamic conditional correlations, appropriate regularity conditions, and associated asymptotic theory. This enables checking of internal consistency and...

Persistent link: https://www.econbiz.de/10011531101

Long memory modelling of inflation with stochastic variance and structural breaks

Bos, Charles S.; Koopman, Siem Jan; Ooms, Marius - 2007

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...

Persistent link: https://www.econbiz.de/10011373822

Likelihood functions for state space models with diffuse initial conditions

Francke, Marc K.; Koopman, Siem Jan; Vos, Aart F. de - 2008

State space models with nonstationary processes and fixed regression effects require a state vector with diffuse initial conditions. Different likelihood functions can be adopted for the estimation of parameters in time series models with diffuse initial conditions. In this paper we consider...

Persistent link: https://www.econbiz.de/10011374403

Parameter driven multi-state duration models : simulated vs. approximate maximum likelihood estimation

Monteiro, André Antonio - 2008 - This version: February 15, 2008

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews,...

Persistent link: https://www.econbiz.de/10011374420

A general framework for observation driven time-varying parameter models

Creal, Drew; Koopman, Siem Jan; Lucas, André - 2008

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/10011377309

Dynamic factor models with smooth loadings for analyzing the term structure of interest rates

Jungbacker, Borus; Koopman, Siem Jan; Wel, Michel van der - 2010

We propose a new approach to the modelling of the term structure of interest rates. We consider the general dynamic … set of unsmoothed Fama-Bliss zero yields for US treasuries of different maturities. The general dynamic factor model with …

Persistent link: https://www.econbiz.de/10011378359

A non-Gaussian panel time series model for estimating and decomposing default risk

Koopman, Siem Jan; Lucas, André; Daniels, Robert - 2005

decomposed into a systematic and firm-specific risk component, where the systematic component reflects the general economic …

Persistent link: https://www.econbiz.de/10011343953

Model-based measurement of latent risk in time series with applications

Bijleveld, Frits; Commandeur, Jacques Jean François; … - 2005

we introduce a general multivariate framework for the time series analysis of risk that is modelled as a latent process … present a general model for the analysis of risk and discuss its statistical treatment based on linear state space methods … the general methodology can be effectively used in the assessment of risk. …

Persistent link: https://www.econbiz.de/10011348356