arXiv 14 Jul 2021 · Statistics — Methodology · publishedJournal of Econometrics (2022) · 25 citations (OpenAlex)
arXiv:2107.06663 · PDF · DOI · OpenAlex · Extracted main text
This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but have non-standard asymptotics. Second, the disaster shock is identified as the component with the largest kurtosis and whose impact effect is negative. An estimator that is robust to infinite variance is used to recover the mutually independent components. Third, an independence test on the residuals pre-whitened by the Choleski decomposition is proposed to test the restrictions imposed on a SVAR. The test can be applied whether the data have fat or thin tails, and to over as well as exactly identified models. Three applications are considered. In the first, the independence test is used to shed light on the conflicting evidence regarding the role of uncertainty in economic fluctuations. In the second, disaster shocks are shown to have short term economic impact arising mostly from feedback dynamics. The third uses the framework to study the dynamic effects of economic shocks post-covid.
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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 | Davis and Fernandes (2022) Independent Component Analysis with Heavy Tails using Distance Covariance | 0.941 | 6 | 3 | 83% |
| 2 | Matteson and Tsay (2017) Independent Component Analysis via Distance Covariance | 0.874 | 5 | 2 | 100% |
| 3 | Davis and Resnick (1986) Limit Theory for the Sample Correlation Function of Moving Averages self | 0.737 | 4 | 2 | 75% |
| 4 | Chen and Bickel (2006) Efficient Independent Component Analysis | 0.693 | 5 | 1 | 100% |
| 5 | Gouriéroux, Monfort, and Renne (2017) Statistical Inference for Independent Component Analysis: Application to Structural VAR Models | 0.644 | 4 | 1 | 100% |
| 6 | Bach and Jordan (2001) Kernel Independent Component Analysis | 0.644 | 2 | 2 | 100% |
| 7 | Stock and Watson (2015) Factor Models for Macroeconomics | 0.644 | 2 | 2 | 100% |
| 8 | Davis, Matsui, Mikosch, and Wan (2018) Applications of distance correlation to time series | 0.511 | 4 | 2 | 25% |
| 9 | Székely, Rizzo, and Bakirov (2007) Measuring and Testing Dependence by Correlation of Distances | 0.511 | 3 | 2 | 33% |
| 10 | Hastie and Tibshirani (2003) Independent Component Analysis Through Product Density Estimation | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification and Estimation of Simultaneous Equation Models Using Higher-Order Cumulant Restrictions | 1.000 | 7 | 3 |
| 2 | Structural Analysis of Vector Autoregressive Models | 0.405 | 1 | 1 |