Testing Structural Changes in Panel Data with Small Fixed Panel Size and Bootstrap

TL;DR

This paper develops a bootstrap-based testing procedure for detecting common mean changes in panel data with small observations per panel, providing a data-driven approach that does not require variance estimation.

Contribution

It introduces a new ratio test statistic for small-panel data, proves its asymptotic properties, and proposes a bootstrap method for improved inference without tuning parameters.

Findings

Test statistic is asymptotically valid under null hypothesis.

Bootstrap method is shown to be valid and data-driven.

The approach effectively detects common structural changes in panel data.

Abstract

Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. This paper establishes testing procedures to detect a possible common change in means of the panels. To this end, we consider a ratio type test statistic and derive its asymptotic distribution under the no change null hypothesis. Moreover, we prove the consistency of the test under the alternative. The main advantage of such an approach is that the variance of the observations neither has to be known nor estimated. On the other hand, the correlation structure is required to be calculated. To overcome this issue, a bootstrap technique is proposed in the way of a completely data driven approach without any tuning parameters. The validity of the bootstrap algorithm is shown. As a by-product of the developed tests, we introduce a common break point estimate and prove its consistency. The results are illustrated through a simulation study. An application of the procedure to actuarial data is presented.