Academic Editor: Kelvin K. W. Yau
Copyright © 2011 Jose A. Campillo-García and Daniel Ventosa-Santaulària. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We consider two well-known facts in econometrics: (i) the failure of the
orthogonality assumption (i.e., no independence between the regressors
and the error term), which implies biased and inconsistent Least Squares (LS)
estimates and (ii) the consequences of using nonstationary variables, acknowledged
since the seventies; LS might yield spurious estimates when the
variables do have a trend component, whether stochastic or deterministic.
In this work, an optimistic corollary is provided: it is proven that the LS
regression, employed in nonstationary and cointegrated variables where the
orthogonality assumption is not satisfied, provides estimates that converge to
their true values. Monte Carlo evidence suggests that this property is maintained
in samples of a practical size.