Norges Bank

Working Paper

Business cycle analysis and VARMA models

by Christian Kascha and Karel Mertens
Working Paper

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.

Norges Bank’s working papers present research projects and reports that are generally not in their final form. Other analyses by Norges Bank’s economists are also included in the series. The views and conclusions in these documents are those of the authors.

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ISSN 1502-8190 (online)

Published 30 April 2008 08:30
Published 30 April 2008 08:30