This paper proposes a new approach for model selection and applies it to a classical time series modeling problem. In contrast to conventional model selection methods like AIC and BIC, whose penalty terms typically depend only on the number of model parameters, the proposed model selection method also takes the values of the model parameters and the sets of candidate models into account. A brief sketch of a Bayesian further development of this method is given within the framework of the linear regression model.