Search Results for author:
Found 12 papers, 1 papers with code
Predictive Density Combination Using a Tree-Based Synthesis Function
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods.
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks
The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails.
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting.
Bayesian Modeling of TVP-VARs Using Regression Trees
This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance.
Forecasting US Inflation Using Bayesian Nonparametric Models
The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks.
Large Order-Invariant Bayesian VARs with Stochastic Volatility
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 Cholesky decomposition for the error covariance matrix.
Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model
We develop a Bayesian non-parametric quantile panel regression model.
Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions
In a forecasting exercise involving a large macroeconomic data set we find that combining VARs with factor models using our prior can lead to forecast improvements.
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs
Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets.
Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs
This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees.
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Time-varying parameter (TVP) regression models can involve a huge number of coefficients.
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
In this paper, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables.
