Unit root bootstrap tests for AR (1) models

Abstract

We propose bootstrap tests for unit roots in first-order autoregressive models and we establish their asymptotic validity both for independent and for autoregressive errors; in this case, the bootstrap methodology directly approaches the asymptotic distribution, making unnecessary the usual corrections due to dependence of innovations. We also present a Monte Carlo power study comparing these tests with existing alternative methods. For small samples, the power of the bootstrap test outperforms that of previous proposals.