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Large Bayesian VARs for Binary, Censored and Count Variables
Joshua C. C. Chan, Michael Pfarrhofer, Davide Pettenuzzo +2
We extend the vector autoregression (VAR) to model binary, censored, count and unrestricted variables jointly, treating missing observations of any type in the same way. Each restr…
Large Bayesian Tensor VARs with Stochastic Volatility
Joshua C. C. Chan, Yaling Qi
We consider Bayesian tensor vector autoregressions (TVARs) in which the VAR coefficients are arranged as a three-dimensional array or tensor, and this coefficient tensor is paramet…
BVARs and Stochastic Volatility
Joshua Chan
Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatili…
Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility
Joshua C. C. Chan, Xuewen Yu
We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that…
Asymmetric Conjugate Priors for Large Bayesian VARs
Joshua C. C. Chan
Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulati…