Imputation-based semiparametric estimation for INAR(1) processes with missing data

Authors : Wei XİONG, Dehui WANG, Xinyang WANG

Pages : 1843-1864

Doi:10.15672/hujms.643081

View : 17 | Download : 9

Publication Date : 2020-10-06

Article Type : Research Paper

Abstract :In applied problems parameter estimation with missing data has risen as a hot topic. Imputation for ignorable incomplete data is one of the most popular methods in integer-valued time series. For data missing not at random insert ignore into journalissuearticles values(MNAR);, estimators directly derived by imputation will lead results that is sensitive to the failure of the effectiveness. In view of the first-order integer-valued autoregressive insert ignore into journalissuearticles values(INARinsert ignore into journalissuearticles values(1);); processes with MNAR response mechanism, we consider an imputation based semiparametric method, which recommends the complete auxiliary variable of Yule-Walker equation. Asymptotic properties of relevant estimators are also derived. Some simulation studies are conducted to verify the effectiveness of our estimators, and a real example is also presented as an illustration.
Keywords : integer valued autoregressive, semiparametric likelihood, first step imputation, missing not at random