Modified Stationarity Tests With Data-Dependent Model-Selection Rules
提出改进平稳性检验的方法，通过提高检验在单位根备择假设下的收敛速度并优化自回归阶数选择，模拟和实际数据（美国月度失业率）验证了有效性。
We describe some simple methods for improving the performance of stationarity tests (i.e., tests that have a stationary null and a unit-root alternative). Specifically, we increase the rate of convergence of the test under the unit-root alternative from O p(T) to O p (T 2), then suggest an optimal method of selecting the order of the autoregressive component in the fitted autoregressive integrated moving average model on which the test is based. Simulation evidence suggests that these modifications work well. We apply the modified procedure to U.S. monthly macroeconomic data and uncover new evidence of a unit root in unemployment.