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Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data
Blasques, Francisco; Hoogerkamp, Meindert Heres; … - 2021
We propose a dynamic factor model which we use to analyze the relationship between education participation and national … unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor … loadings associated with the dynamic macroeconomic factor, we can measure to what extent the different types of education …
Persistent link: https://www.econbiz.de/10012427178
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Dynamic factor models with clustered loadings : forecasting education flows using unemployment data
Blasques, Francisco; Hoogerkamp, Meindert Heres; … - 2020
We propose a dynamic factor model which we use to analyze the relationship between education participation and national … unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor … loadings associated with the dynamic macroeconomic factor, we can measure to what extent the different types of education …
Persistent link: https://www.econbiz.de/10012315409
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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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Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes
Blasques, Francisco; Koopman, Siem Jan; Lucas, Andre - 2012
We characterize the dynamic properties of Generalized Autoregressive Score (GAS) processes by identifying regions of the parameter space that imply stationarity and ergodicity. We show how these regions are affected by the choice of parameterization and scaling, which are key features of GAS...
Persistent link: https://www.econbiz.de/10010326396
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Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
Hol, Eugenie; Koopman, Siem Jan - 2000
In this paper we compare the predictive abilility of Stochastic Volatility (SV)models to that of volatility forecasts implied by option prices. We develop anSV model with implied volatility as an exogeneous var able in the varianceequation which facilitates the use of statistical tests for...
Persistent link: https://www.econbiz.de/10010324427
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The Stochastic Volatility in Mean Model
Koopman, Siem Jan; Uspensky, Eugenie Hol - 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/10010324578
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Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices
Carnero, M. Angeles; Koopman, Siem Jan; Ooms, Marius - 2003
Although the main interest in the modelling of electricity prices is often on volatility aspects, we argue that stochastic heteroskedastic behaviour in prices can only be modelled correctly when the conditional mean of the time series is properly modelled. In this paper we consider different...
Persistent link: https://www.econbiz.de/10010324825
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Stock Index Volatility Forecasting with High Frequency Data
Hol, Eugenie; Koopman, Siem Jan - 2002
The increasing availability of financial market data at intraday frequencies has not only led to the development of 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...
Persistent link: https://www.econbiz.de/10010324972
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Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Koopman, Siem Jan; Bos, Charles S. - 2002
The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters related to the stochasticprocesses of the mean part...
Persistent link: https://www.econbiz.de/10010324992
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Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Koopman, Siem Jan; Lucas, Andre; Scharth, Marcel - 2011
We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...
Persistent link: https://www.econbiz.de/10010325813