18 citations · 47 across the 7 of their papers we have counts for
7 papers
High-Dimensional Conditionally Gaussian State Space Models with Missing Data
Joshua C. C. Chan, Aubrey Poon, Dan Zhu
We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two im…
Comparing Stochastic Volatility Specifications for Large Bayesian VARs
Joshua C. C. Chan
Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics. One main difficulty for practitioner…
Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis
Joshua Chan, Eric Eisenstat, Xuewen Yu
Vector autoregressions (VARs) with multivariate stochastic volatility are widely used for structural analysis. Often the structural model identified through economically meaningful…
Large Hybrid Time-Varying Parameter VARs
Joshua C. C. Chan
Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these m…
Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach
Joshua C. C. Chan, Aubrey Poon, Dan Zhu
State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially…
Large Order-Invariant Bayesian VARs with Stochastic Volatility
Joshua C. C. Chan, Gary Koop, Xuewen Yu
Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cho…