AI For Trading: Heteroskedasticity (18)

Heteroskedasticity

One of the assumptions of linear regression is that its input data are homoscedastic(同方差的). A visual way to check if the our data is homoscedastic is a scatter plot (like the one we saw in the video).

If our data is heteroscedastic(异方差性), a linear regression estimate of the coefficients may be less accurate (further from the actual value), and we may get a smaller p-value than should be expected, which means we may assume (incorrectly) that we have an accurate estimate of the regression coefficient, and assume that it’s statistically significant when it’s not.

Breusch-Pagan Test

Note, we’ll cover the Breusch-Pagan test for heteroscedasticity in more detail after we learn about regression.

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