I'm building a logistic regression model in which almost all of the input variables are categorical. There are multiple sets of categorical variables, for example, day of the week, age range buckets, occupation types, etc. So it may look something like this:
SAT, SUN, MON, TUE, WED, THU, (FRI is omitted as a base variable) AGE_0_to_10, AGE_11_to_20, AGE_21_to_30, AGE_31_to_40, AGE_40_plus (omitted as base) MANUFACTURING, HEALTHCARE, FINANCE, RETAIL, SERVICES, TRANSPORTATION, COMMUNICATIONS, UTILITIES (omitted as base)
I am testing for multicollinearity using the VIF and I notice that there are usually high VIFs among the sets of categorical variables. For example, the days of the week would all have high VIFs and removing one or more fixes that.
Is this normal and expected since they do have some sort of relationship already?
NOTE: The actual variable categories are not the ones I am using, but just have provided textbook examples for ease of understanding. Not saying that days of the week are correlated in a logistic regression setting.