arXiv 27 Oct 2025 · Econometrics
arXiv:2510.23540 · PDF · DOI · OpenAlex · Extracted main text
This paper discusses the different contemporaneous causal interpretations of Panel Vector Autoregressions (PVAR). I show that the interpretation of PVARs depends on the distribution of the causing variable, and can range from average treatment effects, to average causal responses, to a combination of the two. If the researcher is willing to postulate a no residual autocorrelation assumption, and some units can be thought of as controls, PVAR can identify average treatment effects on the treated. This method complements the toolkits already present in the literature, such as staggered-DiD, or LP-DiD, as it formulates assumptions in the residuals, and not in the outcome variables. Such a method features a notable advantage: it allows units to be “sparsely” treated, capturing the impact of interventions on the innovation component of the outcome variables. I provide an example related to the evaluation of the effects of natural disasters economic activity at the weekly frequency in the US.I conclude by discussing solutions to potential violations of the SUTVA assumption arising from interference.
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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 | Rambachan, A. and Shephard, N (2021) When do common time series estimands have nonparametric causal meaning? | 0.928 | 5 | 5 | 80% |
| 2 | Callaway, B., Goodman-Bacon, A., and Sant'Anna, P. H. C (2021) Difference-in-differences with a continuous treatment | 0.874 | 5 | 2 | 100% |
| 3 | Dube, A., Girardi, D., Jorda, O., and Taylor, A. M (2023) A local projections approach to difference-in-differences event studies | 0.811 | 4 | 2 | 100% |
| 4 | Xu, R (2023) Difference-in-differences with interference: A finite population perspective | 0.644 | 4 | 1 | 100% |
| 5 | Cavallo, E., Galiani, S., Noy, I., and Pantano, J (2013) Catastrophic Natural Disasters and Economic Growth | 0.644 | 2 | 2 | 100% |
| 6 | Bojinov, I. and Shephard, N (2019) Time series experiments and causal estimands: exact randomization tests and trading | 0.644 | 2 | 2 | 100% |
| 7 | Sun, L. and Abraham, S (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 8 | Usman, S., Fernández, G. G.-T., and Parker, M (2025) Going nuts: The regional impact of extreme climate events over the medium term | 0.644 | 2 | 2 | 100% |
| 9 | Baumeister, C., Leiva-León, D., and Sims, E (2024) Tracking weekly state-level economic conditions | 0.585 | 3 | 1 | 100% |
| 10 | Callaway, B. and Sant'Anna, P. H (2021) Difference-in-differences with multiple time periods | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 39 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 | 0.737 | 4 | 2 |
| 2 | Control VAR: a counterfactual based approach to inference in macroeconomics | 0.405 | 1 | 1 |