Identifying stationary series in panels: A Monte Carlo evaluation of sequential panel selection methods

- We investigate the merits of sequential panel selection methods in classifying individual time series into nonstationary and stationary ones.
- A Monte Carlo analysis based on simulating individual unit root asymptotic test statistics and p values is carried out.
- We illustrate the simulation results using Receiver Operating Characteristic (ROC) graphs.
- Sequential panel selection methods may outperform unit root time series tests only under rather special conditions.

Sequential panel selection methods (spsms - procedures that sequentially use conventional panel unit root tests to identify I(0) time series in panels) are increasingly used in the empirical literature. We check the reliability of spsms by using Monte Carlo simulations based on generating directly the individual asymptotic p values to be combined into the panel unit root tests, in this way isolating the classification abilities of the procedures from the small sample properties of the underlying univariate unit root tests. The simulations consider both independent and cross-dependent individual test statistics. Results suggest that spsms may offer advantages over time series tests only under special conditions.