Choosing Predictors for the Location Model

The process of choosing predictors for the location component of the model is similar to the process of selecting predictors in a linear regression model. You should take both theoretical and empirical considerations into account in selecting predictors. Ideally, your model would include all of the important predictors and none of the others. In practice, you often don't know exactly which predictors will prove to be important until you build the model. In that case, it's usually better to start off by including all of the predictors that you think might be important. If you discover that some of those predictors seem not to be helpful in the model, you can remove them and reestimate the model.

In this case, previous experience and some preliminary exploratory analysis have identified five likely predictors: age, duration of loan, number of credits at the bank, other installment debts, and housing type. You will include these predictors in the initial analysis and then evaluate the importance of each predictor. Number of credits, other installment debts, and housing type are categorical predictors, entered as factors in the model. Age and duration of loan are continuous predictors, entered as covariates in the model.

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