On Jun 4, 2:27 pm, sonik son <sonik...@gmail.com> wrote: > It sounds to me as though you may not be doing "modeling" in the SAS > sense of the word. True modeling is taking historic data to make > predictions about future behavior. Unless you intend on predicting, in > your case, balance decay or some other performance measure for your BY > groups, then you might not be doing true modeling and may not need a > SAS PROC at all. > > Take a look at PROC REG (for continuous variables) and PROC LOGISTIC > (for discrete values) as a starting point. Those will give you > coefficients to evaluate the partial effect of the right-hand-side > variables on your dependent variable. > > Good luck! Sounds like a fun challenge. > > On Jun 4, 11:16 am, BJMurphy <murphy....@gmail.com> wrote: > > > > > I have a relatively straightforward modeling question that I was > > hoping to get some preliminary suggestions about (read: "I'm sort of a > > n00b"). I have some high level time series data for accounts--both > > dollar balance and a frequency count for each month going back about 5 > > years. Part of the project involves tracking the accounts from > > inception and modeling the decline in balance or frequency of the > > accounts in groups according to the month they were acquired and the > > account type. This decline is expected to follow the form of > > exponential decay, and I'd like to verify that with some kind of > > modeling. > > > The data has the following columns: > > procurement month -- the first month this account showed up > > actual month -- the actual month for which the data applies > > age in months -- the difference between the first two dates, > > essentially, expressed in number of months > > account type -- a grouping mechanism for describing what type of > > accounts these are > > balance -- the total balance in dollars > > count -- the total frequency of accounts > > > Here are my concerns: > > > 1) the data has group variables for account type, which I would assume > > can fall into a BY statement in whatever PROC I decide to use, but I > > need to make sure that such a statement exists (or a comparable way of > > modeling the data exists) because the groupings are materially > > different > > > 2) the data can change over time, and hence, I must factor into the > > modeling the time when the account was acquired; I'm unsure if this > > would be appropriately modeled as a time series factor, a variable in > > a BY statement, or some other way > > > 3) the frequency of accounts for a given account type and procurement > > month is going to be monotonically decreasing as you track them > > forward in time; hence, the most plausible options for modeling the > > decay of the frequency are probably linear decay and exponential > > decay--although more complicated (polynomial?) models might fit > > better, I'd like to limit the results to one of these two forms > > > There are so many different ways to piece together the data that I'm > > not sure what the best structure is for modeling, and I'm not sure > > which PROCs within SAS are the best for this type of modeling. In > > general, I can find my way around the PROCs themselves, but there are > > so many different modeling related PROCs, especially because you could > > consider this time series data and you could model it otherwise, that > > I'm really unsure of the best place to start looking. > > > So, short story long, what is the best way to model data like this? > > Thanks in advance.- Hide quoted text - > > - Show quoted text -

Well, I realize that I didn't describe a typical modeling process. If you think of each group of accounts as its own curve, where you graph either dollars or count on the y-axis and the month of the data on the x-axis, then we will have a separate line for each combination of account type and starting month. I want to adaquately fit each of these curves with a simple exponential decay model and a simple linear model, and then compare the two. That part is not very difficult, and doesn't really require any modeling. However, after this, I want to be able to see if these curves form a predictable pattern going forward.

So if the oldest data is 5 years old, then we have 60 data points on the line for that data. Ultimately, after fitting curves to each of the lines, I want to see if there is something in the data that shows some kind of pattern in the curves we can fit to each line. That is, since the line graphs represent data getting newer and newer, can we adaquately predict what the curve will look like for data that is newer?

Maybe that helps illuminate the need for SAS PROCs. Thanks again for any suggestions.