We propose an easy technique to test for time-variation in coefficients and volatili-ties. Specifically, by using a noncentered parameterization for state space models, we develop a method to directly calculate the relevant Bayes factor using the Savage-Dickey density ratio—thus avoiding the computation of the marginal likelihood al-together. The proposed methodology is illustrated via two empirical applications. In the first application we test for time-variation in the volatility of inflation in the G7 countries. The second application investigates if there is substantial time-variation in the NAIRU in the US
This paper provides a general methodology for testing for dependence in time series data, with parti...
We propose a factor model which allows a parsimonious representation of the time series evolution of...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
© 2016 Taylor & Francis Group, LLC. We propose an easy technique to test for time-variation in coeff...
This article develops a new econometric methodology for performing stochastic model specification se...
This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (...
© 2017 American Statistical Association. This article generalizes the popular stochastic volatility ...
This paper develops a specification test for stochastic volatility models by comparing the nonparame...
We develop importance sampling methods for computing two popular Bayesian model comparison criteria,...
This article develops a new econometric methodology for performing stochastic model specification se...
We consider stochastic volatility models using piecewise constant parameters. We suggest a hybrid op...
We develop importance sampling methods for computing two popular Bayesian model comparison criteria,...
textabstractIn this paper, we make use of state space models to investigate the presence of stochast...
There are both theoretical and empirical reasons for believing that the parameters of macroeconomic ...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
This paper provides a general methodology for testing for dependence in time series data, with parti...
We propose a factor model which allows a parsimonious representation of the time series evolution of...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
© 2016 Taylor & Francis Group, LLC. We propose an easy technique to test for time-variation in coeff...
This article develops a new econometric methodology for performing stochastic model specification se...
This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (...
© 2017 American Statistical Association. This article generalizes the popular stochastic volatility ...
This paper develops a specification test for stochastic volatility models by comparing the nonparame...
We develop importance sampling methods for computing two popular Bayesian model comparison criteria,...
This article develops a new econometric methodology for performing stochastic model specification se...
We consider stochastic volatility models using piecewise constant parameters. We suggest a hybrid op...
We develop importance sampling methods for computing two popular Bayesian model comparison criteria,...
textabstractIn this paper, we make use of state space models to investigate the presence of stochast...
There are both theoretical and empirical reasons for believing that the parameters of macroeconomic ...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...
This paper provides a general methodology for testing for dependence in time series data, with parti...
We propose a factor model which allows a parsimonious representation of the time series evolution of...
In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stoc...