An application of the RINAR(1) process

Abstract

We introduce a new class of autoregressive models for integervalued time series using the rounding operator. Compared to classical INAR models based on the thinning operator, the new models have several advantages: simple innovation structure; autoregressive coefficients with arbitrary signs; possible negative values for time series; possible negative values for the autocorrelation function. Focused on the first order RINAR(1) model, we give conditions for its ergodicity and stationarity. For parameter estimation, a least squares estimator is introduced and we prove its consistency under suitable identifiability condition. An analysis of real data set is carried out to access the performance of the model. © 2009 IFAC.