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~isPartOf:"Working papers / Federal Reserve Bank of Boston"
~language:"eng"
~person:"Florax, Raymond J. G. M."
~person:"Gil-Alaña, Luis A."
~person:"Heckman, James J."
~person:"Koopman, Siem Jan"
~person:"Ravazzolo, Francesco"
~person:"Schmitz, Martin"
~subject:"Factor analysis"
~subject:"Kreditrisiko"
~subject:"Maximum-Likelihood-Schätzung"
~subject:"Meta-Analyse"
~subject:"Prognoseverfahren"
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~subject:"Time series analysis"
~subject:"USA"
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~subject:"Volatility"
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~subject:"Zustandsraummodell"
~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.
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41
Long memory modelling of inflation with stochastic variance and structural breaks
Bos, Charles S.
;
Koopman, Siem Jan
;
Ooms, Marius
-
2007
We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...
Persistent link: https://www.econbiz.de/10011373822
Saved in:
42
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
Saved in:
43
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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44
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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45
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:
46
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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47
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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48
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
Saved in:
49
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:
50
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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