Solved – Testing for heteroskedasticity of time series in R

I wish to test my time series data for volatility clustering, i.e. conditional heteroskedasticity.

So far, I have used the ACF test on the squared and absolute returns of my data, as well as the Ljung-Box test on the squared data (i.e. McLeod.Li.test).

In a recent paper (, the test is reported on page 8) co-authored by a well-known researcher, they have employed the White test ( to directly test for heteroskedasticity.

I have tried the same approach, however was unable to do so.
From my understanding, the White test needs residual variance (usually from a linear regression model) as an input.

Now my question is: How did the researchers perform the White test? I do not understand which inputs they used for their White test.

While searching for solutions, I have found the sandwich package which uses the vcovHC and vcovHAC functions to estimate a heteroskedasticity-consistent covariance matrix, however the input is also a fitted linear regression model..

You should try Mcleod Li Test for heteroskedasticity available in R's TSA package. That package can take just time series as input.

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