Business cycle analysis and VARMA models

Christian Kascha () and Karel Mertens

No 2008/05, Working Paper from Norges Bank

Abstract: Can long-run identified structural vector autoregressions (SVARs) discriminate between competing models in practice? Several authors have suggested SVARs fail partly because they are fiite-order approx-imations to infinite-order processes. We estimate vector autoregressive moving average (VARMA) and state space models, which are not misspecified, using simulated data and compare true with estimated impulse responses of hours worked to a technology shock. We find few gains from using VARMA models. However, state space algorithms can outperform SVARs. In particular, the CCA subspace method consistently yields lower mean squared errors, although even these estimates remain too imprecise for reliable inference. The qualitative differences for algorithms based on different representations are small. The comparison with estimation methods without specification error suggests that the main problem is not one of working with a VAR approximation. The properties of the processes used in the literature make identification via long-run restrictions difficult for any method.

Keywords: SVARs; VARMA; State Space Models; Business cycles (search for similar items in EconPapers)
JEL-codes: C15 C52 E32 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2008-04-30
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://www.norges-bank.no/en/news-events/news-pub ... apers/2008/WP-20085/

Related works:
Journal Article: Business cycle analysis and VARMA models (2009)
Working Paper: Business Cycle Analysis and VARMA models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:bno:worpap:2008_05

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