Long-Lag VARs

Macroeconomic research often relies on structural vector autoregressions,(S)VARs, to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag lengths imply a poor approximation to impor tant data-generating processes (e.g. 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 simulta neously reduces misspecification, which in turn reduces variance. Contrary to conventional wisdom, the trivial solution to the critique actually works. For data generated by frontier DSGE models long-lag VARs are feasible, reduce bias and variance, and have better mean-squared error. Long-lag VARs are also viable in common macroeconomic data and signiÖcantly change structural conclusions about the impact of technology and monetary policy shocks on the economy.