Davide Brignone, Alessandro Franconi, Marco Mazzali
arXiv 12 Jul 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2307.06145 · PDF · DOI · OpenAlex · Extracted main text
External-instrument identification leads to biased responses when the shock is not invertible and the measurement error is present. We propose to use this identification strategy in a structural Dynamic Factor Model, which we call Proxy DFM. In a simulation analysis, we show that the Proxy DFM always successfully retrieves the true impulse responses, while the Proxy SVAR systematically fails to do so when the model is either misspecified, does not include all relevant information, or the measurement error is present. In an application to US monetary policy, the Proxy DFM shows that a tightening shock is unequivocally contractionary, with deteriorations in domestic demand, labor, credit, housing, exchange, and financial markets. This holds true for all raw instruments available in the literature. The variance decomposition analysis highlights the importance of monetary policy shocks in explaining economic fluctuations, albeit at different horizons.
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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 | Bauer and Swanson (2022) A Reassessment of Monetary Policy Surprises and High-Frequency Identification | 0.928 | 4 | 3 | 100% |
| 2 | Forni, Gambetti, Lippi and Sala (2020) Common component structural VARs | 0.928 | 4 | 3 | 100% |
| 3 | Leeper, Walker and Yang (2013) Fiscal foresight and information flows | 0.874 | 6 | 2 | 100% |
| 4 | Forni, Gambetti and Ricco (2022) External Instrument SVAR Analysis for Noninvertible Shocks | 0.830 | 7 | 5 | 57% |
| 5 | Jarociński and Karadi (2020) Deconstructing monetary policy surprises—the role of information shocks | 0.811 | 4 | 2 | 100% |
| 6 | Miescu and Mumtaz (2019) Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy | 0.811 | 4 | 2 | 100% |
| 7 | Mertens and Ravn (2013) The dynamic effects of personal and corporate income tax changes in the United States | 0.737 | 3 | 3 | 67% |
| 8 | Barigozzi, Lippi and Luciani (2021) Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I (1) cointegrated factors | 0.737 | 3 | 2 | 100% |
| 9 | Forni, Giannone, Lippi and Reichlin (2009) Opening the black box: Structural factor models with large cross sections | 0.737 | 3 | 2 | 100% |
| 10 | Forni and Gambetti (2010) The dynamic effects of monetary policy: A structural factor model approach | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 68 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, estimation and inference in Panel Vector Autoregressions using external instruments | 0.405 | 1 | 1 |