Testing for Granger causality in large mixed-frequency VARs
Alain Hecq () and
Stephan Smeekes ()
Journal of Econometrics, 2016, vol. 193, issue 2, 418-432
We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large, implying parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, including bootstrap versions thereof to account for factor estimation uncertainty and improve the finite sample properties of the tests, and a Bayesian VAR extended to mixed frequencies. We compare these methods to a test based on an aggregated model, the max-test (Ghysels et al., 2016a) and an unrestricted VAR-based test (Ghysels et al., 2016b) using Monte Carlo simulations. An empirical application illustrates the techniques.
Keywords: Granger causality; Mixed frequency VAR; Bayesian VAR; Reduced rank model; Bootstrap test (search for similar items in EconPapers)
JEL-codes: C11 C12 C32 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Testing for Granger Causality in Large Mixed-Frequency VARs (2015)
Working Paper: Testing for Granger causality in large mixed-frequency VARs (2015)
Working Paper: Testing for Granger causality in large mixed-frequency VARs (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:eee:econom:v:193:y:2016:i:2:p:418-432
Access Statistics for this article
Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson
More articles in Journal of Econometrics from Elsevier
Series data maintained by Dana Niculescu ().