Giuseppe Cavaliere, Luca Fanelli, Marco Mazzali
arXiv 15 Jul 2026 · Econometrics
arXiv:2607.13879 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel econometric methodology for Structural Vector Autoregressions with external instruments (`proxy-SVARs' or `SVAR-IVs') in panel data characterized by strong cross-sectional dependence, dynamic heterogeneity, and limited availability of direct external instruments for the shocks of interest. For each unit, we specify a Factor-Augmented proxy-SVAR (`proxy-FA-SVAR') that incorporates factors summarizing cross-sectional information from the non-policy variables of the system. The effects of the policy shocks are then recovered indirectly by estimating unit-specific policy reaction functions through a Minimum Distance approach. Identification relies on global instruments for the non-policy shocks; that is, proxies common to all units in the panel, internally constructed from a separate SVAR estimated on factors for the policy and non-policy variables. These global instruments can be complemented with local (idiosyncratic) instruments constructed from auxiliary unit-level SVARs. Their joint use renders the proxy-FA-SVARs overidentified and therefore statistically testable. We illustrate the methodology by estimating government spending multipliers for Italian NUTS-2 regions using annual data. The global and local instruments for the regional output shocks are obtained from Blanchard-Perotti-type SVARs.
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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 | Angelini, Giovanni and Cavaliere, Giuseppe and Fanelli, Luca (2024) An identification and testing strategy for proxy-svars with weak proxies self | 1.000 | 9 | 3 | 100% |
| 2 | Caldara, Dario and Kamps, Christophe (2017) The analytics of SVARs: a unified framework to measure fiscal multipliers | 0.843 | 3 | 3 | 100% |
| 3 | Lucidi, Francesco Simone (2022) The misalignment of fiscal multipliers in Italian regions | 0.737 | 3 | 2 | 100% |
| 4 | Ramey, Valerie A and Zubairy, Sarah (2018) Government spending multipliers in good times and in bad: evidence from US historical data | 0.737 | 3 | 2 | 100% |
| 5 | Canova, Fabio (2024) Should we trust cross-sectional multiplier estimates? | 0.693 | 5 | 1 | 100% |
| 6 | Bernanke, Ben S and Boivin, Jean and Eliasz, Piotr (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach | 0.644 | 2 | 2 | 100% |
| 7 | Blanchard, Olivier and Perotti, Roberto (2002) An empirical characterization of the dynamic effects of changes in government spending and taxes on output | 0.644 | 2 | 2 | 100% |
| 8 | Deleidi, Matteo and Romaniello, Davide and Tosi, Francesca (2021) Quantifying fiscal multipliers in Italy: A Panel SVAR analysis using regional data | 0.644 | 2 | 2 | 100% |
| 9 | Destefanis, Sergio and Di Serio, Mario and Fragetta, Matteo (2022) Regional multipliers across the Italian regions | 0.644 | 2 | 2 | 100% |
| 10 | Mertens, Karel and Ravn, Morten O (2013) The dynamic effects of personal and corporate income tax changes in the United States | 0.644 | 2 | 2 | 100% |
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