Local Projections vs. VARs: Lessons From Thousands of DGPs

Dake Li, Mikkel Plagborg-Møller and Christian Wolf
Additional contact information
Dake Li: Princeton University
Mikkel Plagborg-Møller: Princeton University
Authors registered in the RePEc Author Service: Mikkel Plagborg-Moller

Working Papers from Princeton University. Economics Department.

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.

Keywords: external instrument; impulse response function; local projection; proxy variable; structural vector autoregression (search for similar items in EconPapers)
JEL-codes: C32 C36 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://scholar.princeton.edu/sites/default/files/lp_var_simul.pdf

Related works:
Working Paper: Local Projections vs. VARs: Lessons From Thousands of DGPs (2024)
Working Paper: Local Projections vs. VARs: Lessons From Thousands of DGPs (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2021-55

Access Statistics for this paper

More papers in Working Papers from Princeton University. Economics Department. Contact information at EDIRC.
Bibliographic data for series maintained by Bobray Bordelon ().