  In the class of normal regression models with a finite number of regressors,
and for a wide class of prior distributions, a Bayesian model selection
procedure based on the Bayes factor is consistent [Casella and Moreno J. Amer.
Statist. Assoc. 104 (2009) 1261--1271]. However, in models where the number of
parameters increases as the sample size increases, properties of the Bayes
factor are not totally understood. Here we study consistency of the Bayes
factors for nested normal linear models when the number of regressors increases
with the sample size. We pay attention to two successful tools for model
selection [Schwarz Ann. Statist. 6 (1978) 461--464] approximation to the Bayes
factor, and the Bayes factor for intrinsic priors [Berger and Pericchi J. Amer.
Statist. Assoc. 91 (1996) 109--122, Moreno, Bertolino and Racugno J. Amer.
Statist. Assoc. 93 (1998) 1451--1460]. We find that the the Schwarz
approximation and the Bayes factor for intrinsic priors are consistent when the
rate of growth of the dimension of the bigger model is $O(n^b)$ for $b<1$. When
$b=1$ the Schwarz approximation is always inconsistent under the alternative
while the Bayes factor for intrinsic priors is consistent except for a small
set of alternative models which is characterized.
