全球智库信息集成服务系统(G2TT): IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w21033
来源ID	Working Paper 21033
	IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade
	Denis Chetverikov; Bradley Larsen; Christopher Palmer
发表日期	2015-03-23
出版年	2015
语种	英语
摘要	We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor (1981). Because of the presence of group-level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally simple, consisting of group-by-group quantile regression followed by two-stage least squares. Using the Bahadur representation of quantile estimators, we derive weak conditions on the growth of the number of observations per group that are sufficient for consistency and asymptotic zero-mean normality of our estimator. As in Hausman and Taylor (1981), micro-level covariates can be used as internal instruments for the endogenous group-level treatment if they satisfy relevance and exogeneity conditions. An empirical application indicates that low-wage earners in the US from 1990--2007 were significantly more affected by increased Chinese import competition than high-wage earners. Our approach applies to a broad range of settings in labor, industrial organization, trade, public finance, and other applied fields.
主题	Econometrics ; Estimation Methods ; International Economics ; Trade ; Labor Economics ; Labor Compensation
URL	https://www.nber.org/papers/w21033
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/578705
推荐引用方式 GB/T 7714	Denis Chetverikov,Bradley Larsen,Christopher Palmer. IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade. 2015.