  We consider a stationary linear AR($p$) model with observations subject to
gross errors (outliers). The autoregression parameters as well as the
distribution function (d.f.) $G$ of innovations are unknown. The distribution
of outliers $\Pi$ is unknown and arbitrary, their intensity is $\gamma
n^{-1/2}$ with an unknown $\gamma$, $n$ is the sample size. We test the
hypothesis for normality of innovations $$\mathbf{H}_\Phi \colon G \in
\{\Phi(x/\theta),\,\theta>0\},$$ $\Phi(x)$ is the d.f. $\mathbf{N}(0,1)$. Our
test is the special symmetrized Pearson's type test. We find the power of this
test under local alternatives $$\mathbf{H}_{1n}(\rho)\colon
G(x)=A_n(x):=(1-\rho n^{-1/2})\Phi(x/\theta_0)+\rho n^{-1/2}H(x), $$ $\rho\geq
0,\,\theta_0$ is the unknown (under $\mathbf{H}_\Phi$) variance of innovations.
First of all we estimate the autoregression parameters and then using the
residuals from the estimated autoregression we construct a kind of empirical
distribution function (r.e.d.f.), which is a counterpart of the (inaccessible)
e.d.f. of the autoregression innovations. After this we construct the
symmetrized variant r.e.d.f. Our test statistic is the functional from
symmetrized r.e.d.f. We obtain a stochastic expansion of this symmetrized
r.e.d.f. under $\mathbf{H}_{1n}(\rho)$ , which enables us to investigate our
test. We establish qualitative robustness of this test in terms of uniform
equicontinuity of the limiting power (as functions of $\gamma,\rho$ and $\Pi$)
with respect to $\gamma$ in a neighborhood of $\gamma=0$.
