Causality tests and conditional heteroskedasticity:

Vilasuso, Jon

doi:10.1016/s0304-4076(00)00072-5

Cited by 26 publications

(21 citation statements)

References 22 publications

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“…Moreover, in the presence of conditional heteroskedasticity Vilasuso (2001) investigates the reliability of causality tests based on least squares. He demonstrates that when conditional heteroskedasticity is ignored, least squares causality tests exhibit considerable size distortion if the conditional variances are correlated.…”

Section: Korean Economy and Sub-samplesmentioning

confidence: 99%

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

Karanasos

Kartsaklas

2009

Journal of Empirical Finance

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Section: Korean Economy and Sub-samplesmentioning

confidence: 99%

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

Karanasos

Kartsaklas

2009

Journal of Empirical Finance

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“…7 We measure inflation and output uncertainty by their respective estimated conditional variances. Depending on model specification, causation in mean 6 In the presence of conditional heteroskedasticity, Vilasuso (2001) investigates the reliability of causality tests based on least squares. He demonstrates that when conditional heteroskedasticity is ignored, least squares causality tests exhibit considerable size distortion if the conditional variances are correlated.…”

Section: A Bivariate Garch Model Of Inflation and Output Growthmentioning

confidence: 99%

Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic Performance

Fountas

Karanasos²,

Kim³

2006

Oxf Bull Econ Stat

128

110

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We use a bivariate generalized autoregressive conditionally heteroskedastic (GARCH) model of inflation and output growth to examine the causality relationship among nominal uncertainty, real uncertainty and macroeconomic performance measured by the inflation and output growth rates. The application of the constant conditional correlation GARCH(1,1) model leads to a number of interesting conclusions. First, inflation does cause negative welfare effects, both directly and indirectly, i.e. via the inflation uncertainty channel. Secondly, in some countries, more inflation uncertainty provides an incentive to Central Banks to surprise the public by raising inflation unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic models, business cycle variability and the rate of economic growth are related. More variability in the business cycle leads to more output growth.

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“…Given the assumption of homoskedasticity, this null hypothesis can be examined by using standard Wald F -tests. However, if the homoskedasticity assumption is not satisfied then standard F -tests can frequently lead to false rejections of the null (Vilasuso, 2001). Pavlidis et al (2013) examine the performance of several heteroskedasticity robustification methods for both linear and nonlinear Granger-causality tests.…”

Section: Causality Testsmentioning

confidence: 99%