5 citations · 5 across the 7 of their papers we have counts for
9 papers
Bayesian Markov-Switching Partial Reduced-Rank Regression
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-Rank (RR) regression is a powerful dimensionality reduction technique but it overlooks any possible group configuration among the responses by assuming a low-rank structure…
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…
Static and Dynamic BART for Rank-Order Data
Matteo Iacopini, Eoghan O'Neill, Luca Rossini
Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order dat…
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…
Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-rank regression recognises the possibility of a rank-deficient matrix of coefficients. We propose a novel Bayesian model for estimating the rank of the coefficient matrix,…
Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications
Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of energy commodities, where the homoskedasticity assumption is relaxed to all…