Stationarity with Occasionally Binding Constraints

TL;DR

This paper introduces conditions for the stationarity and ergodicity of CKSVAR models, which handle series with occasionally binding constraints, using less conservative criteria related to the model's stability.

Contribution

It provides new sufficient conditions for stationarity and ergodicity of CKSVAR models, improving upon existing criteria by relating to the stability of the deterministic component.

Findings

Conditions relate to the stability of the deterministic part.

Criteria are less conservative than general VTAR models.

Conditions can be approximated numerically with high precision.

Abstract

This paper studies a class of multivariate threshold autoregressive models, known as censored and kinked structural vector autoregressions (CKSVAR), which are notably able to accommodate series that are subject to occasionally binding constraints. We develop a set of sufficient conditions for the processes generated by a CKSVAR to be stationary, ergodic, and weakly dependent. Our conditions relate directly to the stability of the deterministic part of the model, and are therefore less conservative than those typically available for general vector threshold autoregressive (VTAR) models. Though our criteria refer to quantities, such as refinements of the joint spectral radius, that cannot feasibly be computed exactly, they can be approximated numerically to a high degree of precision.