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Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly

Michal Kolesár, Mikkel Plagborg-Møller

arXiv 15 Nov 2024 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 3 citations (OpenAlex)

arXiv:2411.10415 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Applied macroeconomists frequently use impulse response estimators motivated by linear models. We study whether the estimands of such procedures have a causal interpretation when the true data generating process is in fact nonlinear. We show that vector autoregressions and linear local projections onto observed shocks or proxies identify weighted averages of causal effects regardless of the extent of nonlinearities. By contrast, identification approaches that exploit heteroskedasticity or non-Gaussianity of latent shocks are highly sensitive to departures from linearity. Our analysis is based on new results on the identification of marginal treatment effects through weighted regressions, which may also be of interest to researchers outside macroeconomics.

Citation extraction

81
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Rambachan and Shephard (2021) When do common time series estimands have nonparametric causal meaning?1.00094100%
2Goncalves, Herrera, Kilian, and Pesavento (2024) State-dependent local projections1.00074100%
3Angrist and Kuersteiner (2011) Causal Effects of Monetary Shocks: Semiparametric Conditional Independence Tests with a Multinomial Propensity Score0.92844100%
4Angrist, Jordà, and Kuersteiner (2018) Semiparametric Estimates of Monetary Policy Effects: String Theory Revisited0.92844100%
5Goncalves, Herrera, Kilian, and Pesavento (2024) Nonparametric Local Projections0.92844100%
Caravello2024unmatched citation key Caravello20240.92843100%
7Goldsmith-Pinkham, Hull, and Kolesár (2024) Contamination Bias in Linear Regressions0.92843100%
8Stock and Watson (2018) Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments0.92843100%
9Lewis (2024) Identification Based on Higher Moments0.87472100%
10Lewbel (2012) Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models0.87452100%

Showing the top 10 of 82 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Clustered Local Projections for Time-Varying Models0.87463
22405.189870.64422
3Semiparametric inference for impulse response functions using double/debiased machine learning0.64422
4Residualised Treatment Intensity and the Estimation of Average Partial Effects0.51132
5The purpose of an estimator is what it does: Misspecification, estimands, and over-identification0.40511
6Leniency Designs: An Operator's Manual0.40511