This is the problem i am trying to solve for a client , and would appreciate some help :
1) I am trying to predict the "time to default" (along with the associated probability for a particular loan to default in month1 , month2..etc.) within a six month time period for a set of retail loans.
2) The independent variables are loan characteristics (time-invariant) which will be observed over a period of 12 months prior to default e.g. loan amount , tenure , repayment history etc.
3) The nature of the data is such that the dependant variable (time to default) is observed only in discrete units i.e. 1 month , 2 months , 3 months etc.
After going through some of the available literature regarding survival analysis , I would prefer to use a suitable parametric method e.g. LIFEREG (after determining the appropriate underlying distribution), firstly because the methodology/results are easy to interpret and explain and secondly because prediction (for future loans) and validation appear to be simpler compared to proportional hazard methods.
So my question , what would be the best way to use parametric methods while allowing for discrete time independent variables as above ?