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accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an … volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to … that time-varying volatility is only present in the a selection of idiosyncratic components while the coefficients driving …
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volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …
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Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump …
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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...
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Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent … countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate … particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic …
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