The QLIM Procedure
- Overview
-
Getting Started
-
Syntax
-
DetailsOrdinal Discrete Choice ModelingLimited Dependent Variable ModelsStochastic Frontier Production and Cost ModelsHeteroscedasticity and Box-Cox TransformationBivariate Limited Dependent Variable ModelingSelection ModelsMultivariate Limited Dependent ModelsVariable SelectionTests on ParametersEndogeneity and Instrumental VariablesPanel Data AnalysisBayesian AnalysisPrior DistributionsAutomated MCMCMarginal LikelihoodStandard DistributionsOutput to SAS Data SetOUTEST= Data SetNamingODS Table NamesODS Graphics
-
Examples
- References
Variable Selection
Variable Selection
Variable selection uses either Akaike’s information criterion (AIC) or the Schwartz Bayesian criterion (SBC) and either a forward selection method or a backward elimination method.
Forward selection starts from a small subset of variables. In each step, the variable that gives the largest decrease in the value of the information criterion specified in the CRITER= option (AIC or SBC) is added. The process stops when the next candidate to be added does not reduce the value of the information criterion by more than the amount specified in the LSTOP= option in the MODEL statement.
Backward elimination starts from a larger subset of variables. In each step, one variable is dropped based on the information criterion that is chosen.