Sensitivity Analysis of the Risk of Forecasting for Autoregressive Time Series with Missing Values

Abstract

The problems of statistical forecasting of vector autoregressive time series with missing values are considered for different levels of prior information on the parameters of the underlying model. The mean square risk of forecasting and the risk sensitivity coefficient are evaluated and analyzed. Results of numerical experiments are presented.