Un-truncating VARs

Ferre De Graeve and Andreas Westermark

No 271, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)

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 (search for similar items in EconPapers)
JEL-codes: C18 E37 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2013-06-01
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:rbnkwp:0271

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