5 citations · 15 across the 14 of their papers we have counts for
6 papers · 1 filter
Money Growth and Inflation: A Quantile Sensitivity Approach
Matteo Iacopini, Aubrey Poon, Luca Rossini +1
An innovative method is proposed to construct a quantile dependence system for inflation and money growth. By considering all quantiles and leveraging a novel notion of quantile se…
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
Ruben Loaiza-Maya, Didier Nibbering, Dan Zhu
The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variable…
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence
Yunyun Wang, Tatsushi Oka, Dan Zhu
Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series. In this study, we…
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…
Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP
Matteo Iacopini, Aubrey Poon, Luca Rossini +1
Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content…
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…