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2018

Two-Step Estimation and Inference with Possibly Many Included Covariates

Cattaneo, Matias D., Jansson, Michael, Ma, Xinwei

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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 \textit{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.

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