arXiv 23 Mar 2023 · Econometrics · publishedJournal of Applied Econometrics (2025) · 1 citations (OpenAlex)
arXiv:2303.13281 · PDF · DOI · OpenAlex · Extracted main text
This study proposes a combination of a statistical identification approach with potentially invalid short-run zero restrictions. The estimator shrinks towards imposed restrictions and stops shrinkage when the data provide evidence against a restriction. Simulation results demonstrate how incorporating valid restrictions through the shrinkage approach enhances the accuracy of the statistically identified estimator and how the impact of invalid restrictions decreases with the sample size. The estimator is applied to analyze the interaction between the stock and oil market. The results indicate that incorporating stock market data into the analysis is crucial, as it enables the identification of information shocks, which are shown to be important drivers of the oil price.
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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 | Guay, A (2021) Identification of Structural Vector Autoregressions Through Higher Unconditional Moments | 0.928 | 4 | 3 | 100% |
| 2 | Baumeister, C. and Hamilton, J. D (2019) Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand… | 0.920 | 9 | 3 | 78% |
| 3 | Lanne, M. and Luoto, J (2021) GMM Estimation of Non-Gaussian Structural Vector Autoregression | 0.874 | 7 | 2 | 100% |
| 4 | Mesters, G. and Zwiernik, P (2024) Non-independent components analysis | 0.860 | 11 | 4 | 64% |
| 5 | Braun, R (2023) The importance of supply and demand for oil prices: Evidence from non-gaussianity | 0.843 | 4 | 3 | 75% |
| 6 | Keweloh, S. A (2023) Structural vector autoregressions and higher moments: Challenges and solutions in small samples self | 0.811 | 5 | 2 | 80% |
| 7 | Lanne, M., Meitz, M., and Saikkonen, P (2017) Identification and Estimation of Non-Gaussian Structural Vector Autoregressions | 0.737 | 4 | 3 | 50% |
| 8 | Kilian, L. and Park, C (2009) The Impact of Oil Price Shocks on the US Stock Market | 0.737 | 3 | 2 | 100% |
| 9 | Anttonen, J., Lanne, M., and Luoto, J (2023) Bayesian inference on fully and partially identified structural vector autoregressions | 0.644 | 2 | 2 | 100% |
| 10 | Maxand, S (2020) Identification of independent structural shocks in the presence of multiple gaussian components | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies | 0.737 | 5 | 3 |
| 2 | A large non-Gaussian structural VAR with application to Monetary Policy | 0.405 | 1 | 1 |