arXiv 27 Oct 2025 · Econometrics
arXiv:2510.23762 · PDF · Extracted main text
This paper addresses the challenges of giving a causal interpretation to vector autoregressions (VARs). I show that under independence assumptions VARs can identify average treatment effects, average causal responses, or a mix of the two, depending on the distribution of the policy. But what about situations in which the economist cannot rely on independence assumptions? I propose an alternative method, defined as control-VAR, which uses control variables to estimate causal effects. Control-VAR can estimate average treatment effects on the treated for dummy policies or average causal responses over time for continuous policies. The advantages of control-based approaches are demonstrated by examining the impact of natural disasters on the US economy, using Germany as a control. Contrary to previous literature, the results indicate that natural disasters have a negative economic impact without any cyclical positive effect. These findings suggest that control-VARs provide a viable alternative to strict independence assumptions, offering more credible causal estimates and significant implications for policy design in response to natural disasters.
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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? | 0.950 | 14 | 6 | 86% |
| 2 | Johansen, S (1995) Likelihood-Based Inference in Cointegrated Vector Autoregressive Models | 0.928 | 5 | 3 | 80% |
| 3 | Ludvigson, S. C., Ma, S., and Ng, S (2021) COVID-19 and the costs of deadly disasters | 0.928 | 4 | 3 | 100% |
| 4 | Callaway, B., Goodman-Bacon, A., and Sant'Anna, P. H. C (2021) Difference-in-differences with a continuous treatment | 0.843 | 3 | 3 | 100% |
| 5 | Meinen, P. and Roehe, O (2017) On measuring uncertainty and its impact on investment: Cross-country evidence from the euro area | 0.737 | 3 | 2 | 100% |
| 6 | Nakamura, E. and Steinsson, J (2018) Identification in macroeconomics | 0.644 | 2 | 2 | 100% |
| 7 | Bojinov, I. and Shephard, N (2019) Time series experiments and causal estimands: exact randomization tests and trading | 0.644 | 2 | 2 | 100% |
| 8 | Müller, U. K. and Watson, M. W (2018) Long-run covariability | 0.511 | 2 | 2 | 50% |
| 9 | Jurado, K., Ludvigson, S. C., and Ng, S (2015) Measuring uncertainty | 0.511 | 2 | 1 | 100% |
| 10 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of california tobacco control program | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.