Two-Step Estimation and Inference with Possibly Many Included Covariates

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Jansson, Michael; Ma, Xinwei
• 署名单位: University of Michigan System; University of Michigan; University of California System; University of California Berkeley; CREATES
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdy053
• 发表日期: 2019
• 页码: 1095-1122
• 关键词: small bandwidth asymptotics; semiparametric estimators; instrumental variables; robust regression; Causal Inference; Wild Bootstrap; models; selection; EQUATIONS; variance

摘要

We study the implications of including many covariates in a first-step estimate entering a two-step estimation procedure. We find that a first-order bias emerges when the number of included covariates is large relative to the square-root of sample size, rendering standard inference procedures invalid. We show that the jackknife is able to estimate this many covariates bias consistently, thereby delivering a new automatic bias-corrected two-step point estimator. The jackknife also consistently estimates the standard error of the original two-step point estimator. For inference, we develop a valid post-bias-correction bootstrap approximation that accounts for the additional variability introduced by the jackknife bias-correction. We find that the jackknife bias-corrected point estimator and the bootstrap post-bias-correction inference perform excellent in simulations, offering important improvements over conventional two-step point estimators and inference procedures, which are not robust to including many covariates. We apply our results to an array of distinct treatment effect, policy evaluation, and other applied microeconomics settings. In particular, we discuss production function and marginal treatment effect estimation in detail.