Testing for a Unit Root in Time Series With Pretest Data-Based Model Selection
研究数据驱动的滞后阶数估计对增广迪基-富勒检验行为的影响，推导了检验收敛的条件，并通过模拟和实际数据说明滞后选择方法对单位根推断的敏感性。
In this article we examine the impact of data-based lag-length estimation on the behavior of the augmented Dickey–Fuller (ADF) test for a unit root. We derive conditions under which the ADF test converges to the distribution tabulated by Dickey and Fuller and verify that these conditions are satisfied by several commonly employed lag-selection strategies. Simulation evidence indicates that the performance of the ADF test is considerably improved when the lag length is selected from the data. An application to inventory series illustrates that inference about a unit root can be very sensitive to the method of lag-length selection.