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

Michal Koles\'ar and Mikkel Plagborg-M{\o}ller

Papers from arXiv.org

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.

Date: 2024-11, Revised 2025-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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