observations. It does not require predictions or observations to meet the requirements of interval or ratio measurement scales. In …Most traditional strategies of assessing the fit between a simulation's set of predictions (outputs) and a set of … relevant observations rely either on visual inspection or squared distances among averages. Here we introduce an alternative …

's information criterion (AIC), a modified Mallows' Cp criterion and a modified Bayesian information criterion (BIC). The penalty … terms in these criteria are shown to be different from the usual terms. We prove that the modified BIC consistently selects … the regressors and the number of breaks whereas the modified AIC and the modified Cp criterion tend to overly choose them …

, generalto- specific, minimum t-statistics, BIC and AIC. We propose a new method and show that it has good finite sample … to the number of observations. This makes it infeasible to condition on all variables in order to determine the …

Akaike information criterion (AIC) and the Bayesian information criterion (BIC). This paper considers several topics: how AIC …, the effect of using a maximum lag on model selection, and the forecasting performance of combining AR(4), AIC, and BIC … and BIC choose lags in autoregressive models on actual series, how models so selected forecast relative to an AR(4) model …

maximization of log-likelihood with Cp, AIC, and BIC penalties, bootstrap and cross-validation error estimation, and coefficient …

-statistics, BIC and AIC. We prove that the problem in general is ill-posed and that the existing methods are inconsistent. We propose … observations. This makes it impossible to condition on all regressors when determining if a variable is important. We investigate …

, BIC, AIC and general-tospecific. We propose a new method and show that it is well behaved compared to existing methods. … variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to …