Impact of Model Specification Decisions on Unit Root Tests

Performance of unit tests depends on several specification decisions prior to their application e.g., whether or not to include a deterministic trend. Since there is no standard procedure for making such decisions, therefore the practitioners routinely make several arbitrary specification decisions. In Monte Carlo studies, the design of DGP supports these decisions, but for real data, such specification decisions are often unjustifiable and sometimes incompatible with data. We argue that the problems posed by choice of initial specification are quite complex and the existing voluminous literature on this issue treats only certain superficial aspects of this choice. We also show how these initial specifications affect the performance of unit root tests and argue that Monte Carlo studies should include these preliminary decisions to arrive at a better yardstick for evaluating such tests.