Moment estimation of uncertain autoregressive model and its application in financial market

Abstract

Uncertain autoregressive model is a powerful analytical tool that uses uncertainty theory to predict future values based on previously observed values. In the study of uncertain autoregressive model, one of the core problems is how to estimate the unknown parameters and uncertain disturbance term in the model. In this paper, a moment estimation method for uncertain autoregressive model is proposed to determine these unknown parameters and uncertain disturbance term. Following that, the uncertain hypothesis test is used to verify the suitability of the estimated uncertain autoregressive model. In addition, we also provide a case study of Disney stock prices to illustrate the advantages of moment estimation method over other statistical inference methods.