Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/126130
Authors: 
Götz, Thomas B.
Hecq, Alain
Smeekes, Stephan
Year of Publication: 
2015
Series/Report no.: 
Discussion Paper, Deutsche Bundesbank 45/2015
Abstract: 
We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, and implement bootstrap versions to account for the uncertainty when estimating factors and to improve the finite sample properties of these tests. We also consider a Bayesian VAR that we carefully extend to the presence of mixed frequencies. We compare these methods to an aggregated model, the max-test approach introduced by Ghysels et al. (2015a) as well as to the unrestricted VAR using Monte Carlo simulations. The techniques are illustrated in an empirical application involving daily realized volatility and monthly business cycle fluctuations.
Subjects: 
Granger Causality
Mixed Frequency VAR
Bayesian VAR
Reduced Rank Model
Bootstrap Test
JEL: 
C11
C12
C32
ISBN: 
978-3-95729-218-6
Document Type: 
Working Paper

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