Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of autoregression coefficients and a probability distribution on the nonnegative integers, called an immigration or innovation distribution. Traditionally, parametric models are considered where the innovation distribution is assumed to belong to a parametric family. This 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 autoregression parameters and the innovation distribution.
|Place of Publication||Tilburg|
|Number of pages||39|
|Publication status||Published - 2008|
|Name||CentER Discussion Paper|
- count data
- nonparametric maximum likelihood
- infinite-dimensional Z-estimator
- semiparametric efficiency
Drost, F. C., van den Akker, R., & Werker, B. J. M. (2008). Efficient Estimation of Autoregression Parameters and Innovation Distributions forSemiparametric Integer-Valued AR(p) Models (Revision of DP 2007-23). (CentER Discussion Paper; Vol. 2008-53). Econometrics.