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An hourly periodic state space model for modelling French national electricity load
Dordonnat, V.; Koopman, Siem Jan; Ooms, Marius; … - In: International journal of forecasting 24 (2008) 4, pp. 566-587
Persistent link: https://www.econbiz.de/10003808308
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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 … 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/10014220784
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Likelihood-based analysis for dynamic factor models
Jungbacker, Borus; Koopman, Siem Jan - 2008
We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...
Persistent link: https://www.econbiz.de/10011373811
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The stochastic volatility in mean model
Koopman, Siem Jan; Hol Uspensky, Eugenie - 2000
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...
Persistent link: https://www.econbiz.de/10011303314
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Forecasting the variability of stock index returns with stochastic volatility models and implied volatility
Hol Uspensky, Eugenie; Koopman, Siem Jan - 2000
returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …
Persistent link: https://www.econbiz.de/10011304384
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Low frequency and weighted likelihood solutions for mixed frequency dynamic factor models
Blasques, Francisco; Koopman, Siem Jan; Mallee, Max I. P. - 2014
The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...
Persistent link: https://www.econbiz.de/10010391543
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Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors
Bennedsen, Mikkel; Hillebrand, Eric; Koopman, Siem Jan - In: Energy economics 96 (2021), pp. 1-17
Persistent link: https://www.econbiz.de/10012818590
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Dynamic factor models with clustered loadings : forecasting education flows using unemployment data
Blasques, Francisco; Hoogerkamp, Meindert Heres; … - In: International journal of forecasting 37 (2021) 4, pp. 1426-1441
Persistent link: https://www.econbiz.de/10013274289
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Bayesian risk forecasting for long horizons
Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019
We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and …
Persistent link: https://www.econbiz.de/10011979983
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Periodic unobserved cycles in seasonal time series with an application to US unemployment
Koopman, Siem Jan; Ooms, Marius; Hindrayanto, Irma - 2006
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/10011350384