- Title
- Local Projections vs. VARs: Lessons From Thousands of DGPs
- Author(s)
- Dake Li Dake Li (Princeton University)
- Mikkel Plagborg-Møller Mikkel Plagborg-Møller (Princeton University)
- Christian K. Wolf Christian Wolf (University of Chicago)
- Abstract
- We conduct a simulation study of 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. Our 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.
- Creation Date
- 2021-03
- Section URL ID
- Paper Number
- 2021-55
- URL
- https://scholar.princeton.edu/sites/default/files/lp_var_simul.pdf
- File Function
- Jel
- C32, C36
- Keyword(s)
- external instrument, impulse response function, local projection, proxy variable, structural vector autoregression
- Suppress
- false
- Series
- 13