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Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
Koopman, Siem Jan; Lucas, Andre; Scharth, Marcel - Tinbergen Institute - 2012
-driven models. We consider dynamic count, intensity, duration, volatility and copula models, including new specifications that have …
Persistent link: https://www.econbiz.de/10009653053
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Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Ardia, David; Hoogerheide, Lennart F. - Tinbergen Institute - 2010
This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-<I>t</I> innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning a MCMC sampling...</i>
Persistent link: https://www.econbiz.de/10008838647
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Stock Index Volatility Forecasting with High Frequency Data
Hol, Eugenie; Koopman, Siem Jan - Tinbergen Institute - 2002
improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …
Persistent link: https://www.econbiz.de/10005136957
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Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Koopman, Siem Jan; Lucas, Andre; Scharth, Marcel - Tinbergen Institute - 2011
We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. By combining existing numerical and Monte Carlo integration methods, we obtain a general and efficient likelihood evaluation method for this class of models. Our approach is based on the idea that only...
Persistent link: https://www.econbiz.de/10008873337
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Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices
Koopman, Siem Jan; Ooms, Marius; Carnero, M. Angeles - Tinbergen Institute - 2005
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
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Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices
Carnero, M. Angeles; Koopman, Siem Jan; Ooms, Marius - Tinbergen Institute - 2003
Although the main interest in the modelling of electricity prices is often on volatility aspects, we argue that … towards volatility features of the time series. For the older electricity market of Nord Pool in Norway, it is found that a …
Persistent link: https://www.econbiz.de/10005137027
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Complete and incomplete econometric models
Geweke, John - 2010
Persistent link: https://www.econbiz.de/10013503197
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Cross- and Auto-Correlation Effects arising from Averaging: The Case of US Interest Rates and Equity Duration
Hallerbach, Winfried G. - Tinbergen Institute - 2000
Most of the available monthly interest data series consist of monthly averages of daily observations. It is well- known that this averaging introduces spurious autocorrelation effects in the first differences of the series. It is exactly this differenced series we are interested in when...
Persistent link: https://www.econbiz.de/10005209446
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Bias Correction in a Stable AD(1,1) Model
Giersbergen, Noud P.A. van - Tinbergen Institute - 2001
This paper compares the behaviour of a bias-corrected estimator assuming strongly exogenous regressors to the behaviour of a bias-corrected estimator assuming weakly exogenous regressors, when in fact the marginal model contains a feedback mechanism. To this end, the effects of a feedback...
Persistent link: https://www.econbiz.de/10005209462
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A General Framework for Observation Driven Time-Varying Parameter Models
Creal, Drew; Koopman, Siem Jan; Lucas, André - Tinbergen Institute - 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/10005209514