arXiv 16 Jul 2021 · Econometrics · publishedJournal of Applied Econometrics (2023) · 5 citations (OpenAlex)
arXiv:2107.07804 · PDF · DOI · OpenAlex · Extracted main text
Macroeconomists using large datasets often face the choice of working with either a large Vector Autoregression (VAR) or a factor model. In this paper, we develop methods for combining the two using a subspace shrinkage prior. Subspace priors shrink towards a class of functions rather than directly forcing the parameters of a model towards some pre-specified location. We develop a conjugate VAR prior which shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage as well as the number of factors. After establishing the theoretical properties of our proposed prior, we carry out simulations and apply it to US macroeconomic data. Using simulations we show that our framework successfully detects the number of factors. In a forecasting exercise involving a large macroeconomic data set we find that combining VARs with factor models using our prior can lead to forecast improvements.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chan (2020) Large bayesian VARs: A flexible kronecker error covariance structure | 1.000 | 5 | 3 | 100% |
| 2 | Banbura, Giannone, and Reichlin (2010) Large Bayesian vector auto regressions | 0.928 | 4 | 3 | 100% |
| 3 | Giannone, Lenza, and Primiceri (2015) Prior selection for vector autoregressions | 0.928 | 4 | 3 | 100% |
| 4 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.644 | 2 | 2 | 100% |
| 5 | Kadiyala and Karlsson (1997) Numerical methods for estimation and inference in bayesian var-models | 0.644 | 2 | 2 | 100% |
| 6 | Shin, Bhattacharya, and Johnson (2020) Functional horseshoe priors for subspace shrinkage | 0.644 | 2 | 2 | 100% |
| 7 | McCracken and Ng (2020) Fred-qd: A quarterly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 8 | Bernanke, Boivin, and Eliasz (2005) Measuring the effects of monetary policy: A factor augmented vector autoregressive (FAVAR) approach | 0.405 | 1 | 1 | 100% |
| 9 | Bhattacharya and Dunson (2011) Sparse bayesian infinite factor models | 0.405 | 1 | 1 | 100% |
| 10 | Chan, Koop, Tobias, and Poirier (2019) Bayesian Econometric Methods: Cambridge University Press | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Shrinkage in High-Dimensional VAR Models: A Comparative Study | 0.511 | 2 | 1 |
| 2 | Forecasting US Inflation Using Bayesian Nonparametric Models | 0.405 | 1 | 1 |
| 3 | Coarsened Bayesian VARs Correcting BVARs for Incorrect Specification | 0.405 | 1 | 1 |
| 4 | Nonlinear Dynamic Factor Analysis With a Transformer Network | 0.405 | 1 | 1 |