Bootstrap Cointegration Tests in ARDL Models

TL;DR

This paper introduces a bootstrap method for Pesaran, Shin, and Smith's bound tests in ARDL models, improving inference accuracy and addressing issues like size distortion and inconclusive results.

Contribution

It develops a bootstrap approach for bound tests in ARDL models, enhancing reliability over traditional asymptotic methods and highlighting the risks of model misspecification.

Findings

Bootstrap tests outperform traditional bound tests in simulations.

Proper model specification is crucial for accurate inference.

Bootstrap methods reduce size distortion and inconclusiveness.

Abstract

The paper proposes a new bootstrap approach to the Pesaran, Shin and Smith's bound tests in a conditional equilibrium correction model with the aim to overcome some typical drawbacks of the latter, such as inconclusive inference and distortion in size. The bootstrap tests are worked out under several data generating processes, including degenerate cases. Monte Carlo simulations confirm the better performance of the bootstrap tests with respect to bound ones and to the asymptotic F test on the independent variables of the ARDL model. It is also proved that any inference carried out in misspecified models, such as unconditional ARDLs, may be misleading. Empirical applications highlight the importance of employing the appropriate specification and provide definitive answers to the inconclusive inference of the bound tests when exploring the long-term equilibrium relationship between economic variables.