Template-type: ReDif-Paper 1.0
Author-Name: Götz T.B. 
Author-Name: Hecq A.W. 
Author-workplace-name: GSBE
Title: Testing for Granger causality in large mixed-frequency VARs
Abstract: In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels 2012, where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a high-frequency one and vice versa. 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 two approaches to solve this problem, reduced rank restrictions and a Bayesian mixed-frequency VAR. For the latter, we extend the approach in Banbura et al. 2010 to a mixed-frequency setup, which presents an alternative to classical Bayesian estimation techniques. We compare these methods to a common aggregated low-frequency model as well as to the unrestricted VAR in terms of their Granger non-causality testing behavior using Monte Carlo simulations. The techniques are illustrated in an empirical application involving dailyrealized volatility and monthly business cycle fluctuations.
Keywords: Hypothesis Testing: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; 
Classification-JEL: C12; C32; .
Series:  Research Memorandum
Creation-Date: 2014
Number: 028
File-URL: http://pub.maastrichtuniversity.nl/bd9faf12-2a67-483d-8ed9-01bef7cb121c
File-Format: application/pdf
File-Size: 854488
Handle: RePEc:unm:umagsb:2014028