Two-stage procedure in a first-order autoregressive process and comparison with a purely sequential procedure. Issue 4 (2nd October 2021) - Sajjadipanah, Soudabe; Mahmoudi, Eisa; Zamani, Mohammadsadegh
Abstract: A two-stage procedure in a first-order autoregressive model ( AR ( 1 ) ) is considered that investigates the point and the interval estimation of parameters based on the least squares estimator. The two-stage procedure is shown to be as effective as the best fixed-sample-size procedure. In this regard, the significant properties of the procedure, such as asymptotic risk efficiency, asymptotic efficiency, and asymptotic consistency, are established. A Monte Carlo simulation study is conducted to compare the performance of the two-stage procedure and the purely sequential procedure. Finally, real-time series data are considered to illustrate the applicability of the two-stage procedure.