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**Lag length selection and the construction of unit root tests with good size and power.**
*(English)*
Zbl 1056.62529

Summary: It is widely known that when there are errors with a moving-average root close to \(-1\), a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (\(k\)) that is very small. We consider a class of Modified Information Criteria (MIC) with a penalty factor that is sample dependent. It takes into account the fact that the bias in the sum of the autoregressive coefficients is highly dependent on \(k\) and adapts to the type of deterministic components present. We use a local asymptotic framework in which the moving-average root is local to \(-1\) to document how the MIC performs better in selecting appropriate values of \(k\). In Monte-Carlo experiments, the MIC is found to yield huge size improvements to the \(\text{DF}^{\text{GLS}}\) and the feasible point optimal PT test developed by Elliott, Rothenberg and Stock (1996). We also extend the M tests developed by Perron and Ng (1996) to allow for GLS detrending of the data. The MIC along with GLS detrended data yield a set of tests with desirable size and power properties.