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The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models...
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Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common...
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stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models … nonlinear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly …
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The time series fit of dynamic stochastic general equilibrium (DSGE) models often suffers from restrictions on the long-run dynamics that are at odds with the data. Relaxing these restrictions can close the gap between DSGE models and vector autoregressions. This paper modifies a simple...
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estimation method that takes these probabilistic inferences into account when relating state variables to observed data. In an …
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