Testing for common cycles in non-stationary VARs with varied frecquency data
This paper proposes a new way for detecting the presence of common cyclical featureswhen several time series are observed/sampled at different frequencies, hence generalizingthe common-frequency approach introduced by Engle and Kozicki 1993 and Vahid andEngle 1993. We start with the mixed-frequency VAR representation investigated in Ghysels2012 for stationary time series. For non-stationary time series in levels, we showthat one has to account for the presence of two sets of long-run relationships. The First setis implied by identities stemming from the fact that the differences of the high-frequencyI1 regressors are stationary. The second set comes from possible additional long-run relationshipsbetween one of the high-frequency series and the low-frequency variables. Ourtransformed VECM representations extend the results of Ghysels 2012 and are very importantfor determining the correct set of variables to be used in a subsequent commoncycle investigation. This has some empirical implications both for the behavior of the teststatistics as well as for forecasting. Empirical analyses with the quarterly real GNP andmonthly industrial production indices for, respectively, the U.S. and Germany illustrate ournew approach. This is also investigated in a Monte Carlo study, where we compare our proposedmixed-frequency models with models stemming from classical temporal aggregationmethods.
|Date of creation:||2013|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +31 (0)43 38 83 830
Web page: http://www.maastrichtuniversity.nl/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:unm:umagsb:2013002. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Charles Bollen)
If references are entirely missing, you can add them using this form.