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~isPartOf:"Discussion paper / Tinbergen Institute"
~language:"eng"
~person:"Gil-Alaña, Luis A."
~person:"Heckman, James J."
~person:"Klaassen, Franc"
~person:"Koopman, Siem Jan"
~subject:"Kreditrisiko"
~subject:"Maximum-Likelihood-Schätzung"
~subject:"Schätzung"
~subject:"USA"
~subject:"United States"
~type:"book"
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Gil-Alaña, Luis A.
Heckman, James J.
Klaassen, Franc
Koopman, Siem Jan
Lucas, André
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21
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
Saved in:
22
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:
23
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:
24
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:
25
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:
26
Business and default cycles for credit risk
Koopman, Siem Jan
;
Lucas, André
-
2003
Persistent link: https://www.econbiz.de/10001792714
Saved in:
27
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:
28
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:
29
A general framework for observation driven time-varying parameter models
Creal, Drew
;
Koopman, Siem Jan
;
Lucas, André
-
2008
Persistent link: https://www.econbiz.de/10003787160
Saved in:
30
The analysis of stochastic volatility in the presence of daily realised measures
Koopman, Siem Jan
;
Scharth, Marcel
-
2011
Persistent link: https://www.econbiz.de/10010191086
Saved in:
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