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~isPartOf:"Discussion paper / Tinbergen Institute"
~isPartOf:"Discussion paper series / UCL Economics"
~isPartOf:"HWWA discussion paper"
~isPartOf:"Working paper / Austrian Center for Labor Economics and the Analysis of the Welfare State"
~language:"eng"
~person:"Florax, Raymond J. G. M."
~person:"Gil-Alaña, Luis A."
~person:"Heckman, James J."
~person:"Koopman, Siem Jan"
~subject:"Kreditrisiko"
~subject:"Maximum-Likelihood-Schätzung"
~subject:"Meta-Analyse"
~subject:"Prognoseverfahren"
~subject:"Schätzung"
~subject:"USA"
~subject:"Volatilität"
~type:"book"
~type_genre:"Collection of articles written by one author"
~type_genre:"Handbuch"
~type_genre:"Non-commercial literature"
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Florax, Raymond J. G. M.
Gil-Alaña, Luis A.
Heckman, James J.
Koopman, Siem Jan
Lucas, André
41
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27
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ECONIS (ZBW)
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41
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:
42
Observation driven mixed-measurement dynamic factor models with an application to credit risk
Creal, Drew
;
Schwaab, Bernd
;
Koopman, Siem Jan
;
Lucas, …
-
2011
This paper has been accepted for publication in the 'Review of Economics and Statistics'.We propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be...
Persistent link: https://www.econbiz.de/10011383248
Saved in:
43
Numerically accelerated importance sampling for nonlinear non-Gaussian state space models
Koopman, Siem Jan
;
Lucas, André
;
Scharth, Marcel
-
2012
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/10011386179
Saved in:
44
Modeling dynamic volatilities and correlations under skewness and fat tails
Zhang, Xin
;
Creal, Drew
;
Koopman, Siem Jan
;
Lucas, André
-
2011
We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...
Persistent link: https://www.econbiz.de/10011386468
Saved in:
45
Time series models with a common stochastic variance for analysing economic time series
Koopman, Siem Jan
;
Bos, Charles S.
-
2002
Persistent link: https://www.econbiz.de/10001718624
Saved in:
46
The stochastic volatility on mean model : empirical evidence from international stock markets
Koopman, Siem Jan
;
Uspensky, Eugenie Hol
-
2000
Persistent link: https://www.econbiz.de/10001472890
Saved in:
47
Business and default cycles for credit risk
Koopman, Siem Jan
;
Lucas, André
-
2003
Persistent link: https://www.econbiz.de/10001792714
Saved in:
48
Tracking growth and the business cycle : a stochastic common cycle model for the euro area
Azevedo, João Valle e
;
Koopman, Siem Jan
;
Rua, António
-
2003
Persistent link: https://www.econbiz.de/10001792789
Saved in:
49
Non-linearities and fractional integration in the US unemployment rate
Caporale, Guglielmo Maria
(
contributor
); …
-
2004
-
[Elektronische Ressource]
Persistent link: https://www.econbiz.de/10001873870
Saved in:
50
Dynamic factor analysis in the presence of missing data
Jungbacker, Borus
;
Koopman, Siem Jan
;
Wel, Michel van der
-
2009
Persistent link: https://www.econbiz.de/10003813787
Saved in:
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