Specification of Periodic Autocovariance Structures in the Presence of Outliers

Abstract

This paper focuses on the specification of periodic autocovariance structures in the presence of outliers, we evaluate autocovariance structures using various outliers' generating models. The analytical results indicate that outliers affect the estimates of periodic autocovariance function (PACVF) due to biases and inflated standard errors. Robust autocovariance structures that accommodate the influence of outliers in different periodic processes are proposed. We fit AR (1) model using both the conventional and Jacknife autocovariance structures; the latter shows high precision in the standard errors of the estimates. We demonstrate our proposed methodology with the precipitation data from Maun Airport in Botswana, and the empirical study supports our theoretical findings.