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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Pavlidis, E., Paya, I. and Peel, D. (2010) Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form. Studies in Nonlinear Dynamics and Econometrics, 14, Article 3.
Grobys, K. (2015) Size Distortions of the Wild Bootstrapped HCCME-Based LM Test for Serial Correlation in the Presence of Asymmetric Conditional Heteroscedasticity. Empirical Economics, 48, 1189-1202. http://dx.doi.org/10.1007/s00181-014-0817-7