5 citations · 9 across the 7 of their papers we have counts for
7 papers
Soft-Noncrossing Bayesian Panel Quantile Regression for Measuring Climate Tail Risk
Florian Huber, Aubrey Poon, Dan Zhu
We develop a hierarchical Bayesian panel quantile regression model in which unit-specific coefficient paths are smoothed across quantiles by Gaussian processes, while a common time…
Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints
Joshua C. C. Chan, Davide Pettenuzzo, Aubrey Poon +1
Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number o…
A Quantile Nelson-Siegel model
Matteo Iacopini, Aubrey Poon, Luca Rossini +1
We propose a novel framework for modeling the yield curve from a quantile perspective. Building on the dynamic Nelson-Siegel model of Diebold et al. (2006), we extend its tradition…
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…
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…