15.3 Heteroskedasticity

15.3.1 Breusch-Pagan test

A4 implies \[ E(\epsilon_i^2|\mathbf{x_i})=\sigma^2 \]

\[ \epsilon_i^2 = \gamma_0 + x_{i1}\gamma_1 + ... + x_{ik -1}\gamma_{k-1} + error \] and determining whether or not \(\mathbf{x}_i\) has any predictive value

  • if \(\mathbf{x}_i\) has predictive value, then the variance changes over the levels of \(\mathbf{x}_i\) which is evidence of heteroskedasticity
  • if \(\mathbf{x}_i\) does not have predictive value, the variance is constant for all levels of \(\mathbf{x}_i\)

The Breusch-Pagan test for heteroskedasticity would compute the F-test of total significance for the following model

\[ e_i^2 = \gamma_0 + x_{i1}\gamma_1 + ... + x_{ik -1}\gamma_{k-1} + error \] A low p-value means we reject the null of homoskedasticity

However, Breusch-Pagan test cannot detect heteroskedasticity in non-linear form

15.3.2 White test

test heteroskedasticity would allow for a non-linear relationship by computing the F-test of total significance for the following model (assume there are three independent random variables)

\[ e_i^2=\gamma_0 + x_i \gamma_1 + x_{i2}\gamma_2 + x_{i3}\gamma_3 + x_{i1}^2\gamma_4 + x_{i2}^2\gamma_5 + x_{i3}^2\gamma_6 + (x_{i1} \times x_{i2})\gamma_7 + (x_{i1} \times x_{i3})\gamma_8 + (x_{i2} \times x_{i3})\gamma_9 + error \] A low p-value means we reject the null of homoskedasticity

Equivalently, we can compute LM as \(LM = nR^2_{e^2}\) where the \(R^2_{e^2}\) come from the regression with the squared residual as the outcome