Bootstrap Inference in the Presence of Bias
We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obtained by proper implementations of the bootstrap. Specifically, we show that the prepivoting approach of Beran (1987, 1988), originally proposed to deliver higher-order refinements, restores bootstrap validity by transforming the original bootstrap p-value into an asymptotically uniform random variable. We propose two different implementations of prepivoting (plug-in and double bootstrap), and provide general high-level conditions that imply validity of bootstrap inference. To illustrate the practical relevance and implementation of our results, we discuss five applications: (i) a simple location model for i.i.d. data, possibly with infinite variance; (ii) regression models with omitted controls; (iii) inference on a target parameter based on model averaging; (iv) ridge-type regularized estimators; and (v) dynamic panel data models.
Date: 12 May 2023, 14:15 (Friday, 3rd week, Trinity 2023)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Room A or https://zoom.us/j/93054414699?pwd=NEFiL2ZNc0t5N3ZIUTE2VEh5OXhZUT09
Speaker: Giuseppe Cavaliere (University of Bologna)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Daria Ihnatenko