Business Cycle Analysis and VARMA Models

Arbeitspapier

Can long-run identified structural vector autoregressions (SVARs) discriminate between competing models in practice? Several authors have suggested SVARs fail partly because they are finite-order approximations 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.

Object Details

• ISBN: 978-82-7553-433-8
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2008/5

Classification and Topics

• Classification: Wirtschaft ; Business Fluctuations; Cycles ; Statistical Simulation Methods: General ; Model Evaluation, Validation, and Selection
• Subject: VARMA ; SVARs ; state space models ; business cycles

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kascha, Christian ; Mertens, Karel
• Event: Veröffentlichung
• (who): Norges Bank
• (where): Oslo
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/209896
• Last update: 10.03.2025, 11:44 AM CET