Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Solving Linear Rational Expectations Models: A Horse Race
Computational Economics
A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood
Computational Statistics & Data Analysis
On marginal likelihood computation in change-point models
Computational Statistics & Data Analysis
Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models
Computational Statistics & Data Analysis
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Analytical results for reducing the parameter space dimension when computing the marginal likelihood are given for the broad class of dynamic mixture models. These results allow the integration of scale parameters out of the likelihood by Kalman filtering and Gaussian quadrature. The method is simple and improves the accuracy of four marginal likelihood estimators, namely, the Laplace method, the Chib estimator, reciprocal importance sampling, and bridge sampling. For some empirically relevant cases like the local level and the local linear models, the marginal likelihood can be obtained directly without any posterior sampling. Implementation details are given in some examples. Two empirical applications illustrate the gain in accuracy achieved.