Maurizio Daniele, Julie Schnaitmann
arXiv 12 Dec 2019 · Econometrics
arXiv:1912.06049 · PDF · DOI · OpenAlex · Extracted main text
We propose a regularized factor-augmented vector autoregressive (FAVAR) model that allows for sparsity in the factor loadings. In this framework, factors may only load on a subset of variables which simplifies the factor identification and their economic interpretation. We identify the factors in a data-driven manner without imposing specific relations between the unobserved factors and the underlying time series. Using our approach, the effects of structural shocks can be investigated on economically meaningful factors and on all observed time series included in the FAVAR model. We prove consistency for the estimators of the factor loadings, the covariance matrix of the idiosyncratic component, the factors, as well as the autoregressive parameters in the dynamic model. In an empirical application, we investigate the effects of a monetary policy shock on a broad range of economically relevant variables. We identify this shock using a joint identification of the factor model and the structural innovations in the VAR model. We find impulse response functions which are in line with economic rationale, both on the factor aggregates and observed time series level.
appendix boundary found by appendix_command · 57% of the source is main text. Read the extracted text to check this.
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 | Stock, J. H. and M. W. Watson (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics, in | 0.950 | 7 | 3 | 86% |
| 2 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.909 | 8 | 5 | 75% |
| 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 | 5 | 2 | 100% |
| 4 | Bai, J., K. Li, and L. Lu (2016) Estimation and inference of FAVAR models | 0.830 | 14 | 4 | 57% |
| 5 | Forni, M. and L. Gambetti (2010) The dynamic effects of monetary policy: A structural factor model approach | 0.811 | 4 | 2 | 100% |
| 6 | Fan, J., Y. Liao, and M. Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.737 | 3 | 3 | 67% |
| 7 | Bai, J. and K. Li (2012) Statistical analysis of factor models of high dimension | 0.737 | 3 | 2 | 100% |
| 8 | Stock, J. H. and M. W. Watson (2002) b): Macroeconomic forecasting using diffusion indexes | 0.737 | 3 | 2 | 100% |
| 9 | McCracken, M. W. and S. Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 10 | Bai, J. and K. Li (2016) Maximum likelihood estimation and inference for approximate factor models of high dimension | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 39 scored citations.