A0354
Title: Local projections vs. VARs: Lessons from thousands of DGPs
Authors: Mikkel Plagborg-Moller - Princeton University (United States) [presenting]
Christian Wolf - MIT (United States)
Dake Li - Princeton (United States)
Abstract: A simulation study is conducted for Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes (DGPs), designed to mimic the properties of the universe of U.S. macroeconomic data. The analysis considers various structural identification schemes and several variants of LP and VAR estimators, and we pay particular attention to the role of the researcher's loss function. A clear bias-variance trade-off emerges: Because our DGPs are not exactly finite-order VAR models, LPs have lower bias than VAR estimators; however, the variance of LPs is substantially higher than that of VARs at intermediate or long horizons. Unless researchers are overwhelmingly concerned with bias, shrinkage via Bayesian VARs or penalized LPs is attractive.
