We propose an iterative stepwise forward selection procedure to screen a subset of important variables from a large pool of candidate variables in choice-based conjoint analysis. The method involves computing weighted correlations between candidate variables and residuals from the prior best fitting multinomial logit (MNL) model. Candidate variables that pass the screening step are then introduced and the MNL model is refitted using standard MNL software. A series of diagnostic tests is carried out before each iteration cycle. The procedure can be implemented easily using commonly available statistical software. We illustrate the application of the proposed method on a large data set.
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