.png){kind=icon}
Return
BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS
.png){kind=icon}
OA
AI
DOI：10.1111/iere.12623.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
AbstractThis article addresses the issue of inference in time‐varying parameter regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selection algorithm allows for assessing at each time period in the sample which predictors are relevant (or not) for forecasting the dependent variable. The algorithm is used to forecast inflation using over 400 macroeconomic, financial, and global predictors, many of which are potentially irrelevant or short‐lived. The new methodology is able to ensure parsimonious solutions to this high‐dimensional estimation problem, which translate into excellent forecast performance.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
No journal information available
Organization
No organization information available
Cited Papers
No cited papers available