Softplus Affine(1, 1) Processes: Enlarged Stationarity Region, Stationary Unit Roots, and Inference

Abstract

Softplus affine\((1,1)\) models keep the conditional mean positive while allowing coefficients of either sign. We study them under Poisson, negative binomial, gamma and related conditional distributions. A stationary solution with moments of all positive orders exists whenever the coefficient sum is less than one, strictly enlarging previously known sufficient regions. At the unit root, the coefficient sum equals one. Under an invertibility condition, a stationary solution exists in this case if and only if the intercept is negative. At a stationary unit root, the conditional mean has a power-law tail with an explicit index, and the observations can exhibit long memory. For a fixed softplus scale, the Poisson quasi-maximum likelihood estimator is consistent and asymptotically normal at the usual \(\sqrt n\) rate below and at the unit root, with the coefficient sum left unrestricted in estimation. At the unit root, these results require only a finite mean and yield standard Wald inference even when the observations have infinite variance or long memory. Simulations and two applications examine finite-sample inference and sensitivity to the softplus scale.