Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly
研究了当真实数据生成过程是非线性时，线性模型驱动的脉冲响应估计量是否仍有因果解释，发现向量自回归和线性局部投影能识别加权平均因果效应，而利用异方差性或非高斯性的方法则高度敏感。
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.