Munich Personal RePEc Archive
metadata only
Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean
Time-varying VAR models represent fundamental tools for the anticipation and analysis of economic crises. Yet they remain subject to a number of limitations. The conventional random walk assumption used for the dynamic parameters appears excessively restrictive, and the existing estimation procedure
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Forecasting VARMA processes using VAR models and subspace-based state space models#1275860
- A Study for Augmented GARCH(p, q) Process#929606
- Bayesian inference, on-line forecasting and model choice for large VAR models with Cholesky stochastic volatility#1063642
- Vector Autoregressions#335827
- Estimating Time-Varying Coefficients With the VC Program#646075
Record · ID 1284363
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive