Local Asymptotic Normality and Efficient Estimation for inar (P) Models

Abstract

Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integervalued phenomena that evolve in time.The distribution of an INAR(p) process is determined by two parameters: a vector of survival probabilities and a probability distribution on the nonnegative integers, called an immigration or innovation distribution.This paper provides an efficient estimator of the parameters, and in particular, shows that the INAR(p) model has the Local Asymptotic Normality property.count data;integer-valued time series;information loss structure