  The issue of model selection in applied research is of vital importance.
Since the true model in such research is not known, which model should be used
from among various potential ones is an empirical question. There might exist
several competitive models. A typical approach to dealing with this is classic
hypothesis testing using an arbitrarily chosen significance level based on the
underlying assumption that a true null hypothesis exists. In this paper we
investigate how successful this approach is in determining the correct model
for different data generating processes using time series data. An alternative
approach based on more formal model selection techniques using an information
criterion or cross-validation is suggested and evaluated in the time series
environment via Monte Carlo experiments. This paper also explores the
effectiveness of deciding what type of general relation exists between two
variables (e.g. relation in levels or relation in first differences) using
various strategies based on hypothesis testing and on information criteria with
the presence or absence of unit roots.
