arXiv 9 Dec 2019 · Statistics — Methodology · 8 citations (OpenAlex)
arXiv:1912.04146 · PDF · DOI · OpenAlex · Extracted main text
A factor-augmented vector autoregressive (FAVAR) model is defined by a VAR equation that captures lead-lag correlations amongst a set of observed variables $X$ and latent factors $F$, and a calibration equation that relates another set of observed variables $Y$ with $F$ and $X$. The latter equation is used to estimate the factors that are subsequently used in estimating the parameters of the VAR system. The FAVAR model has become popular in applied economic research, since it can summarize a large number of variables of interest as a few factors through the calibration equation and subsequently examine their influence on core variables of primary interest through the VAR equation. However, there is increasing need for examining lead-lag relationships between a large number of time series, while incorporating information from another high-dimensional set of variables. Hence, in this paper we investigate the FAVAR model under high-dimensional scaling. We introduce an appropriate identification constraint for the model parameters, which when incorporated into the formulated optimization problem yields estimates with good statistical properties. Further, we address a number of technical challenges introduced by the fact that estimates of the VAR system model parameters are based on estimated rather than directly observed quantities. The performance of the proposed estimators is evaluated on synthetic data. Further, the model is applied to commodity prices and reveals interesting and interpretable relationships between the prices and the factors extracted from a set of global macroeconomic indicators.
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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 | Lin, J. and G. Michailidis (2017) Regularized estimation and testing for high-dimensional multi-block vector-autoregressive models self | 0.928 | 4 | 3 | 100% |
| 2 | Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.928 | 4 | 3 | 100% |
| 3 | Bernanke, B. S., J. Boivin, and P. Eliasz (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach | 0.874 | 8 | 2 | 100% |
| 4 | Bai, J., K. Li, and L. Lu (2016) Estimation and inference of favar models | 0.811 | 4 | 2 | 100% |
| 5 | Basu, S. and G. Michailidis (2015) Regularized estimation in sparse high-dimensional time series models | 0.659 | 21 | 5 | 29% |
| 6 | Bańbura, M., D. Giannone, and L. Reichlin (2010) Large bayesian vector auto regressions | 0.644 | 2 | 2 | 100% |
| 7 | Lütkepohl, H (2005) New Introduction to Multiple Time Series Analysis | 0.644 | 2 | 2 | 100% |
| 8 | Loh, P.-L. and M. J. Wainwright (2012) High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity | 0.630 | 8 | 4 | 25% |
| 9 | Stock, J. H. and M. W. Watson (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics | 0.585 | 3 | 1 | 100% |
| 10 | Frankel, J. A (2008) The effect of monetary policy on real commodity prices | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimation of High-dimensional Nonlinear Vector Autoregressive Models | 0.511 | 2 | 1 |
| 2 | Latent Gaussian dynamic factor modeling and forecasting for multivariate count time series | 0.405 | 1 | 1 |