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~isPartOf:"Discussion paper / Tinbergen Institute"
~isPartOf:"Discussion paper series / UCL Economics"
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~isPartOf:"Working paper series / European Central Bank"
~language:"eng"
~person:"Blasques, Francisco"
~person:"Creal, Drew"
~person:"Florax, Raymond J. G. M."
~person:"Gil-Alaña, Luis A."
~person:"Heckman, James J."
~person:"Koopman, Siem Jan"
~person:"Ooms, Marius"
~person:"Scharth, Marcel"
~subject:"EU countries"
~subject:"Estimation"
~subject:"Forecasting model"
~subject:"Kreditrisiko"
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~subject:"USA"
~subject:"Volatilität"
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Blasques, Francisco
Creal, Drew
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31
Seasonality with trend and cycle interactions in unobserved components models
Koopman, Siem Jan
;
Lee, Kai Ming
-
2008
Unobserved components time series models decompose a time series into a trend, a season, a cycle, an irregular disturbance, and possibly other components. These models have been successfully applied to many economic time series. The standard assumption of a linear model, often appropriate after...
Persistent link: https://www.econbiz.de/10011374413
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32
The effect of the great moderation on the US business cycle in a time-varying multivariate trend-cycle model
Creal, Drew
;
Koopman, Siem Jan
;
Zivot, Eric
-
2008
In this paper we investigate whether the dynamic properties of the U.S. business cycle have changed in the last fifty years. For this purpose we develop a flexible business cycle indicator that is constructed from a moderate set of macroeconomic time series. The coincident economic indicator is...
Persistent link: https://www.econbiz.de/10011376640
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33
Dynamic factor analysis in the presence of missing data
Jungbacker, Borus
;
Koopman, Siem Jan
;
Wel, Michel van der
-
2011
This paper concerns estimating parameters in a high-dimensional dynamic factormodel by the method of maximum likelihood. To accommodate missing data in theanalysis, we propose a new model representation for the dynamic factor model. Itallows the Kalman filter and related smoothing methods to...
Persistent link: https://www.econbiz.de/10011377572
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34
Macro, industry and frailty effects in defaults : the 2008 credit crisis in perspective
Koopman, Siem Jan
;
Lucas, André
;
Schwaab, Bernd
-
2010
We determine the magnitude and nature of systematic default risk using 1971{2009) default data from Moody's. We disentangle systematic risk factors due to business cycle effects, common default dynamics (frailty), and industry-specific dynamics (including contagion). To quantify the contribution...
Persistent link: https://www.econbiz.de/10011379607
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35
Models with time-varying mean and variance : a robust analysis of US industrial production
Bos, Charles S.
;
Koopman, Siem Jan
-
2010
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/10011379641
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36
Modeling trigonometric seasonal components for monthly economic time series
Hindrayanto, Irma
;
Aston, John A.D.
;
Koopman, Siem Jan
; …
-
2010
The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this paper we explore a generalisation of the basic structural time series model in which the time-varying trigonometric terms associated with different seasonal...
Persistent link: https://www.econbiz.de/10011379642
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37
A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations
Creal, Drew
;
Koopman, Siem Jan
;
Lucas, André
-
2010
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/10011380135
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38
Systemic risk diagnostics
Schwaab, Bernd
;
Lucas, André
;
Koopman, Siem Jan
-
2010
, and the rest of the
world
. Controlling for global,region-specific, and industry effects, we construct coincident measures …
Persistent link: https://www.econbiz.de/10011382067
Saved in:
39
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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40
Forecasting the variability of stock index returns with stochastic volatility models and implied volatility
Hol Uspensky, 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/10011304384
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