Conditional L 1 estimation for random coefficient integer-valued autoregressive processes

Summary In this paper we study the integer-valued autoregressive model, which belongs to the class of thinning models with count data.We mainly focus on the random coefficient integer-valued autoregressive (RCINAR) model and propose a conditional least absolute deviation (CL 1 ) method to estimate the parameters of the model. The asymptotic distribution of the CL 1 estimator is investigated. The finite sample performance of the proposed estimator is evaluated through simulation, and is compared with that of conditional least squares (CL 2 ) estimation method. Simulation results show that the proposed method is effective and robust against outliers