论文标题
ARDL模型中的Bootstrap协整测试
Bootstrap Cointegration Tests in ARDL Models
论文作者
论文摘要
本文提出了一种在条件平衡校正模型中针对Pesaran，Shin和Smith的界限测试的新的引导方法，其目的是克服后者的某些典型缺点，例如不确定的推论和大小失真。在几个数据生成过程（包括退化案例）下，进行了引导测试。蒙特卡洛模拟证实了对界面测试以及在ARDL模型的自变量上的渐近F检验的更好性能。还可以证明，在诸如无条件ardls之类的错误指定模型中进行的任何推论都可能具有误导性。经验应用突出了采用适当规范的重要性，并在探索经济变量之间的长期平衡关系时，为有限测试的不确定推断提供了明确的答案。
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.
