Variable selection for ordered categorical data in regression analysis: Information criteria vs. lasso

Variable selection in regression analysis with ordered categorical variables can be simplified by integrating some categories and introducing transformed dummy variables. This allows for the application of traditional variable selection criteria and lasso estimation. In this study, we compare the consistency of information criteria and lasso estimation through simulation studies and empirical example. The results show that BIC and adaptive lasso perform similarly in terms of whether the true number of explanatory variables is selected.