Non-linear dimension reduction in factor-augmented vector autoregressions

DOI10.1016/j.jedc.2023.104800MaRDI QIDQ6558551

Karin Klieber

Publication date: 19 June 2024

Published in: Journal of Economic Dynamics and Control (Search for Journal in Brave)

zbMATH Keywords

dimension reductionmachine learningimpulse response analysisCOVID-19uncertainty shockmonetary policy shocknon-linear factor-augmented vector autoregression

Mathematics Subject Classification ID

Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models
• Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity
• Multilayer feedforward networks are universal approximators
• Nowcasting in a pandemic using non-parametric mixed frequency VARs
• Corrigendum to ``Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors
• Variable prioritization in nonlinear black box methods: a genetic association case study
• Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors
• Econometric analysis of structural systems with permanent and transitory shocks
• Bayesian Approximate Kernel Regression With Variable Selection
• Structural Vector Autoregressions With Nonnormal Residuals
• The Impact of Uncertainty Shocks
• Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
• Deep learning for finance: deep portfolios
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Structural Vector Autoregressive Analysis
• Machine Learning for Data Science Handbook
• Classical time varying factor-augmented vector auto-regressive models -- estimation, forecasting and structural analysis

This page was built for publication: Non-linear dimension reduction in factor-augmented vector autoregressions