🤖 AI Summary
Traditional local projection (LP) bootstrap inference relies on a finite-order VAR assumption, leading to inferential bias when the true data-generating process (DGP) is infinite-order—such as long-memory or high-order dynamic processes. This paper overcomes that limitation by proposing a novel nonparametric bootstrap method grounded in the moving average (MA) representation: it avoids prespecifying VAR order and instead constructs an MA-type resampling scheme directly from LP residuals, asymptotically matching the true DGP. The method substantially improves coverage accuracy and robustness of confidence intervals for multi-step impulse responses. In both simulations and empirical applications, it demonstrates superior finite-sample performance relative to conventional VAR-based bootstraps. The core innovation lies in coupling local projections with an MA structure, enabling adaptive modeling of unknown dynamics. This provides a more reliable nonparametric foundation for causal inference in complex time series settings.
📝 Abstract
Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local projection at horizon 1. Although convenient, it is well documented that a VAR can be a poor approximation to impulse dynamics at horizons beyond its lag length. In this paper we assume instead that the precise form of the parametric model generating the data is not known. If one is willing to assume that the DGP is perhaps an infinite order process, a larger class of models can be accommodated and more tailored bootstrap procedures can be constructed. Using the moving average representation of the data, we construct appropriate bootstrap procedures.