Efficient estimation of autoregression parameters and innovation distributions for semiparametric integer-valued AR(p) models
Drost,F.C. van den Akker,R. Werker,B.J.M.
Drost,F.C.
van den Akker,R.
Werker,B.J.M.
Abstract
Integer-valued auto-regressive (INAR) processes have been introduced to model non-negative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of auto-regression coefficients and a probability distribution on the non-negative integers, called an immigration or innovation distribution. Traditionally, parametric models are considered where the innovation distribution is assumed to belong to a parametric family. The paper instead considers a more realistic semiparametric INAR(p) model where there are essentially no restrictions on the innovation distribution. We provide an (semiparametrically) efficient estimator of both the auto-regression parameters and the innovation distribution.
Description
Appeared earlier as CentER DP 2008-53
Date
2009
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Drost, F C, van den Akker, R & Werker, B J M 2009, 'Efficient estimation of autoregression parameters and innovation distributions for semiparametric integer-valued AR(p) models', Journal of the Royal Statistical Society Series B-Statistical Methodology, vol. 71, no. 2, pp. 467-485.