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This article explores how the choice of lag length affects the ADF test for detecting unit roots in time series data.

October 1, 1994

Testing for a Unit Root in Time Series With Pretest Data-Based Model Selection

Puntos clave

This article explores how the choice of lag length affects the ADF test for detecting unit roots in time series data.
Derived conditions for ADF test distribution convergence under various lag-selection strategies.
Conducted simulations to evaluate ADF test performance based on different lag-length selections.
Applied findings to analyze inventory series for unit root inference sensitivity.
ADF test performance significantly improves with data-driven lag length selection.
Simulation results confirm that many lag-selection methods satisfy necessary convergence conditions.
Real-world application shows that lag-length choice can dramatically affect unit root conclusions.

Resumen

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.

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