Long-Lag VARs

Ferre De Graeve () and Andreas Westermark ()
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Ferre De Graeve: KU Leuven
Andreas Westermark: Research Department, Central Bank of Sweden, Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden

No 451, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)

Abstract: 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.

Keywords: VAR; SVAR; Lag-length; Lag truncation (search for similar items in EconPapers)
JEL-codes: C18 E37 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2025-05-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:rbnkwp:0451

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