Stationarity against integration in the autoregressive process with polynomial trend

TL;DR

This paper investigates the stationarity testing of autoregressive processes with polynomial trends, extending existing tests, analyzing their limitations, and proposing corrections based on asymptotic distribution analysis.

Contribution

It generalizes the LMC test for polynomial trends, derives its asymptotic distribution under null and alternative hypotheses, and corrects its limitations with simulated validation.

Findings

The asymptotic distribution of the test statistic is derived for any polynomial trend.

The LMC and KPSS tests can mistakenly accept stationarity when a unit root is at -1.

Proposed corrections improve test accuracy, validated through simulations.

Abstract

We tackle the stationarity issue of an autoregressive path with a polynomial trend, and we generalize some aspects of the LMC test, the testing procedure of Leybourne and McCabe. First, we show that it is possible to get the asymptotic distribution of the test statistic under the null hypothesis of trend-stationarity as well as under the alternative of nonstationarity, for any polynomial trend of order $r$. Then, we explain the reason why the LMC test, and by extension the KPSS test, does not reject the null hypothesis of trend-stationarity, mistakenly, when the random walk is generated by a unit root located at $-1$. We also observe it on simulated data and we correct the procedure. Finally, we describe some useful stochastic processes that appear in our limiting distributions.