Un-Truncating VARs
Riksbank Research Paper Series No. 102
Sveriges Riksbank Working Paper Series No. 271
23 Pages Posted: 15 Aug 2013
Date Written: June 2013
Abstract
Macroeconomic research often relies on structural vector autoregressions to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to DSGE-models. Empirically, short lag-length is deemed necessary as increased parametrization induces excessive uncertainty. The paper shows that this argument is incomplete. Longer lag-length simultaneously reduces misspecification, which in turn reduces variance. For data generated by frontier DSGE-models long-lag VARs are feasible, reduce bias and variance, and have better coverage. Thus, contrary to conventional wisdom, the trivial solution to the critique actually works.
Keywords: VAR, SVAR, Lag-length, Truncation
JEL Classification: C18, E37
Suggested Citation: Suggested Citation