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Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Sveriges Riksbank Working Paper Series No. 451
Verlag: 
Sveriges Riksbank, Stockholm
Zusammenfassung: 
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 important 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 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. Long-lag VARs are also viable in common macroeconomic data and applications. Thus, contrary to conventional wisdom, the trivial solution to the critique actually works.
Schlagwörter: 
VAR
SVAR
Lag-length
Lag truncation
JEL: 
C18
E37
Dokumentart: 
Working Paper
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