Testing for Common Breaks in a Multiple Equations System

TL;DR

This paper develops a flexible statistical test for detecting whether multiple equations in a multivariate system share common break points, accommodating various regressors and trends, with proven good finite-sample performance.

Contribution

It introduces a general framework for testing common breaks in multivariate systems, allowing integrated regressors, trends, and small sample regimes, with theoretical and simulation validation.

Findings

Test has good finite sample properties.

Framework accommodates integrated regressors and trends.

Applicable to inflation measures and level shifts.

Abstract

The issue addressed in this paper is that of testing for common breaks across or within equations of a multivariate system. Our framework is very general and allows integrated regressors and trends as well as stationary regressors. The null hypothesis is that breaks in different parameters occur at common locations and are separated by some positive fraction of the sample size unless they occur across different equations. Under the alternative hypothesis, the break dates across parameters are not the same and also need not be separated by a positive fraction of the sample size whether within or across equations. The test considered is the quasi-likelihood ratio test assuming normal errors, though as usual the limit distribution of the test remains valid with non-normal errors. Of independent interest, we provide results about the rate of convergence of the estimates when searching over all possible partitions subject only to the requirement that each regime contains at least as many observations as some positive fraction of the sample size, allowing break dates not separated by a positive fraction of the sample size across equations. Simulations show that the test has good finite sample properties. We also provide an application to issues related to level shifts and persistence for various measures of inflation to illustrate its usefulness.