05-26-2015 01:33 PM
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 ?
05-26-2015 02:49 PM
I'm sure you can get more help with the analysis part but I'm wondering if prepping the data would help. Does this get you anywhere closer to where you want to be?
infile cards dsd;
informat date mmddyy10.;
format date mmddyy10.;
input date time_to_default;
format default_date mmddyy10.;
default_date = intnx('month',date,time_to_default,'e');