Performing Granger non-causality test in a VAR (vector autoregressive) framework
assumes the endogenous variables entering into the VAR system to be stationary (i.e.
I(0)). Conversely, estimating a VAR system using non-stationary variables (i.e. integrated
of order one, I(1)) may result bias in inference. Therefore, it is necessary to examine the
time series properties (i.e. the degree of integration, I(d)) of real OFDI and real GDP in
this study. Two different unit root tests are applied in order to assume consistency,
namely the Phillips-Perron (PP) unit root test (Phillips and Perron, 1988) and
Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationary test (Kwiatkowski et al., 1992).
The former assumes each series to be a unit root under the null and allows for
heterogeneous residuals of a unit root process, while the latter assumes the null is
stationary.
Performing Granger non-causality test in a VAR (vector autoregressive) frameworkassumes the endogenous variables entering into the VAR system to be stationary (i.e.I(0)). Conversely, estimating a VAR system using non-stationary variables (i.e. integratedof order one, I(1)) may result bias in inference. Therefore, it is necessary to examine thetime series properties (i.e. the degree of integration, I(d)) of real OFDI and real GDP inthis study. Two different unit root tests are applied in order to assume consistency,namely the Phillips-Perron (PP) unit root test (Phillips and Perron, 1988) andKwiatkowski-Phillips-Schmidt-Shin (KPSS) stationary test (Kwiatkowski et al., 1992).The former assumes each series to be a unit root under the null and allows forheterogeneous residuals of a unit root process, while the latter assumes the null isstationary.
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