Hecq, Alain and Goetz, Thomas (2018): Granger causality testing in mixedfrequency Vars with possibly (co)integrated processes.

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Abstract
We analyze Granger causality testing in mixedfrequency VARs with possibly (co)integrated time series. It is well known that conducting inference on a set of parameters is dependent on knowing the correct (co)integration order of the processes involved. Corresponding tests are, however, known to often suffer from size distortions and/or a loss of power. Our approach, which boils down to the mixedfrequency analogue of the one by Toda and Yamamoto (1995) or Dolado and Lutkepohl (1996), works for variables that are stationary, integrated of an arbitrary order, or cointegrated. As it only requires an estimation of a mixedfrequency VAR in levels with appropriately adjusted lag length, after which Granger causality tests can be conducted using simple standard Wald test, it is of great practical appeal. We show that the presence of nonstationary and trivially cointegrated highfrequency regressors (Goetz et al., 2013) leads to standard distributions when testing for causality on a parameter subset, without any need to augment the VAR order. Monte Carlo simulations and two applications involving the oil price and consumer prices as well as GDP and industrial production in Germany illustrate our approach.
Item Type:  MPRA Paper 

Original Title:  Granger causality testing in mixedfrequency Vars with possibly (co)integrated processes 
Language:  English 
Keywords:  Mixed frequencies; Granger causality; Hypothesis testing, Vector autoregressions; Cointegration 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models 
Item ID:  87746 
Depositing User:  Prof. Alain Hecq 
Date Deposited:  12 Jul 2018 13:30 
Last Modified:  12 Jul 2018 13:32 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/87746 