Dynamic shrinkage priors for large time-varying parameter regressions using scalable Markov chain Monte Carlo methods
Year of publication: |
2024
|
---|---|
Authors: | Hauzenberger, Niko ; Huber, Florian ; Koop, Gary |
Published in: |
Studies in nonlinear dynamics and econometrics : SNDE ; quarterly publ. electronically on the internet. - Berlin : De Gruyter, ISSN 1558-3708, ZDB-ID 1385261-9. - Vol. 28.2024, 2, p. 201-225
|
Subject: | Bayesian variable selection | dynamic shrinkage prior | global-local shrinkage prior | scalable Markov Chain Monte Carlo | time-varying parameter regression | Bayes-Statistik | Bayesian inference | Markov-Kette | Markov chain | Monte-Carlo-Simulation | Monte Carlo simulation | Schätztheorie | Estimation theory | Regressionsanalyse | Regression analysis |
-
Hauzenberger, Niko, (2023)
-
Bayesian model averaging and principal component regression forecasts in a data rich environment
Ouysse, Rachida, (2016)
-
Semiparametric GARCH via Bayesian model averaging
Chen, Wilson Ye, (2021)
- More ...
-
Predictive density combination using a tree-based synthesis function
Chernis, Tony, (2023)
-
Fast and flexible Bayesian inference in time-varying parameter regression models
Hauzenberger, Niko, (2022)
-
Hauzenberger, Niko, (2023)
- More ...