This paper compares the statistical
properties of time-varying causality tests when errors of variables have
multivariate stochastic volatility (SV). The time-varying causal-ity tests in
this paper are based on a logistic smooth transition autoregressive model. The
compared time-varying causality tests include asymptotic tests,
heteroskedasticity-robust tests, and tests using wild bootstrap. Our simulation
results show that asymptotic tests and heteroskedasticity-robust counterparts
have size distortions under multivariate SV, whereas tests using wild bootstrap
have better size properties regardless of type of error. In particular, the
time-varying causality test with first-order Taylor approximation using wild
bootstrap has better statistical properties.
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