Semi-parametric goodness-of-fit testing for INAR models

TL;DR

This paper introduces a semi-parametric goodness-of-fit test for INAR models that does not require specifying the innovation distribution, improving model validation flexibility and performance.

Contribution

The paper develops a novel semi-parametric test for INAR models based on the joint probability generating function, avoiding parametric assumptions on innovations.

Findings

Test has good power and size properties based on simulations.

Higher-order test statistics improve power.

Effective application to real-world economic data.

Abstract

Among the various models designed for dependent count data, integer-valued autoregressive (INAR) processes enjoy great popularity. Typically, statistical inference for INAR models uses asymptotic theory that relies on rather stringent (parametric) assumptions on the innovations such as Poisson or negative binomial distributions. In this paper, we present a novel semi-parametric goodness-of-fit test tailored for the INAR model class. Relying on the INAR-specific shape of the joint probability generating function, our approach allows for model validation of INAR models without specifying the (family of the) innovation distribution. We derive the limiting null distribution of our proposed test statistic, prove consistency under fixed alternatives and discuss its asymptotic behavior under local alternatives. By manifold Monte Carlo simulations, we illustrate the overall good performance of our testing procedure in terms of power and size properties. In particular, it turns out that the power can be considerably improved by using higher-order test statistics. We conclude the article with the application on three real-world economic data sets.