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We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors …
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The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting...
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censored observations of a latent shadow-rate process, and propose an efficient sampler for Bayesian estimation of such “shadow …
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