The Local Projection Residual Bootstrap for AR(1) Models

Abstract: This paper proposes a local projection residual bootstrap method to construct confidence intervals for impulse response coefficients of AR(1) models. Our bootstrap method is based on the local projection (LP) approach and involves a residual bootstrap procedure applied to AR(1) models. We present theoretical results for our bootstrap method and proposed confidence intervals. First, we prove the uniform consistency of the LP-residual bootstrap over a large class of AR(1) models that allow for a unit root, conditional heteroskedasticity of unknown form, and serially dependent shocks. Then, we prove the asymptotic validity of our confidence intervals over the same class of AR(1) models. Finally, we show that the LP-residual bootstrap provides asymptotic refinements for confidence intervals on a restricted class of AR(1) models relative to those required for the uniform consistency of our bootstrap.

Amilcar Velez
Amilcar Velez
Ph.D. Candidate in Economics

I am a Ph.D. candidate in Economics at Northwestern University. I will join the Department of Economics at Cornell University as a Provost New Faculty Fellow in July 2025 and as an Assistant Professor in July 2026.