Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors
Bibliographic Data
| ID | 21653387 |
|---|---|
| Authors | Simon Beyeler (Swiss National Bank Zurich Switzerland), Sylvia Kaufmann (0000-0003-1918-3321, Study Center Gerzensee Gerzensee Switzerland, corresponding author) |
| Year | 2021 |
| Volume | 36 |
| Issue | 7 |
| Pages | 989-1012 |
| Publication date | 2021-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.2852 |
| OpenAlex | W3180204847 |
| Language | EN |
| Citations received | 3 |
| References cited | 55 |
Induced sparsity in the factor loading matrix identifies the factor basis, while rotational identification is obtained ex post by clustering methods closely related to machine learning. We extract meaningful economic concepts from a high‐dimensional data set, which together with observed variables follow an unrestricted, reduced‐form VAR process. Including a comprehensive set of economic concepts allows reliable, fundamental structural analysis, even of the factor augmented VAR itself. We illustrate this by combining two structural identification methods to further analyze the model. To account for the shift in monetary policy instruments triggered by the Great Recession, we follow separate strategies to identify monetary policy shocks. Comparing ours to other parametric and non‐parametric factor estimates uncovers advantages of parametric sparse factor estimation in a high dimensional data environment. Besides meaningful factor extraction, we gain precision in the estimation of factor loadings
Cluster analysis · Dynamic factor · Econometrics · Economics · Estimation · Factor analysis · Great recession · Machine learning · Macroeconomics · Monetary policy · Parametric statistics · Statistics · Computer Science · Italy: Economic History and Contemporary Issues · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact · Artificial Intelligence
Circlize implements and enhances circular visualization in R
Circos
Discretion versus policy rules in practice
Large Bayesian vector auto regressions
A New Measure of Monetary Shocks
Determining the Number of Factors in Approximate Factor Models
Disentangling the Channels of the 2007–09 Recession
Forecasting Using Principal Components From a Large Number of Predictors
Bayesian Measures of Model Complexity and Fit
Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
Bayesian Dynamic Factor Models and Portfolio Allocation
Combined Density Nowcasting in an Uncertain Economic Environment
The Generalized Dynamic-Factor Model
Coincident and Leading Indicators for the Euro Area
| Citations per year | 1 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |