Linear type conditional specifications for multivariate count variables

TL;DR

This paper explores the compatibility and solutions of linear conditional models for multivariate count data, highlighting the advantages of semi-parametric specifications that focus on conditional expectations.

Contribution

It characterizes solutions for two families of conditional models and advocates for semi-parametric approaches based on conditional expectation structures.

Findings

Few solutions admit non-trivial forms in the studied families.

Linear conditional expectations allow a broader family of models.

Semi-parametric models are preferable for spatial count data.

Abstract

This paper investigates conditional specifications for multivariate count variables. Recently, the spatial count data literature has proposed several conditional models such that the conditional expectations are linear in the conditioning variables. These models are much easier to estimate than existing spatial count models based on Gaussian random field. However, whether or not such conditional specifications are compatible have not been addressed. We investigate two large families of conditional models, that are the compound autoregressive model and the random coefficient integer autoregressive model. We characterize all the solutions to these two families of models at arbitrary dimensions, and find that only a handful of them admit non-trivial solutions. We then show that if we focus on the linearity condition of the conditional expectations only, a considerable larger family of solutions can be obtained. This suggests that for spatial count data modeling, semi-parametric type specifications that impose the conditional expectation structure is preferable.