Assessing local influence in linear regression models with first-order autoregressive or heteroscedastic error structure

The local influence approach to the linear regression model with first-order autoregressive errors is developed and discussed. An advantage of this approach is that it avoids the inappropriate case-deletion diagnostic in the autoregressive model and it also allows simultaneous perturbations on all responses. Analogously, we obtain the local influence diagnostic on the weighted regression parameter estimate when the heteroscedastic error structure is considered.