Dear community experts,
I've faced an interesting situation, which is out of my area of expertise, so I need an advice here from someone more experienced in the field.
The problem is following. Suppose, we have a binary logit model. Let's call it old model. The model is estimated over a set of co-variates and observations. Then, we use the model for log-odds prediction for new observations. The predicted log-odds are saved together with new observations.
Now, we do the following. We take new observations and use them to develop a new model, which is also binary logit, and we use log-odds, predicted by old model as one of the co-variates for the new model, plus some other variables. However, the observations are completely different.
This is the part I cannot completely understand. It looks like we use predicted value to predict another value. I wonder, which shortcomings/advantages it has. I mean, we can do that, but then, we must be careful with confidence intervals and model interpretation, must we not?
I read a bit about 2-stage regression models and instrumental variables, and they do have some similarities to the issue. But, as far as I understand, they run over the same set of observations, or am I wrong?
My area of expertise in statistics is a bit different, so I do not know if I'm correct or not. Does this situation has a name? How do we call such models?
Is there any academical work about similar matters so I can use it as a reference? Or, maybe some nice examples with explanation? Something, like 2-stage logit-logit with different data-sets, but connected to each other in a way I described.
I tried to Google the matter, but it is hard to do when I do not know exactly what to search.
I would appreciate any guideline about the problem.
I've faced an interesting situation, which is out of my area of expertise, so I need an advice here from someone more experienced in the field.
The problem is following. Suppose, we have a binary logit model. Let's call it old model. The model is estimated over a set of co-variates and observations. Then, we use the model for log-odds prediction for new observations. The predicted log-odds are saved together with new observations.
Now, we do the following. We take new observations and use them to develop a new model, which is also binary logit, and we use log-odds, predicted by old model as one of the co-variates for the new model, plus some other variables. However, the observations are completely different.
This is the part I cannot completely understand. It looks like we use predicted value to predict another value. I wonder, which shortcomings/advantages it has. I mean, we can do that, but then, we must be careful with confidence intervals and model interpretation, must we not?
I read a bit about 2-stage regression models and instrumental variables, and they do have some similarities to the issue. But, as far as I understand, they run over the same set of observations, or am I wrong?
My area of expertise in statistics is a bit different, so I do not know if I'm correct or not. Does this situation has a name? How do we call such models?
Is there any academical work about similar matters so I can use it as a reference? Or, maybe some nice examples with explanation? Something, like 2-stage logit-logit with different data-sets, but connected to each other in a way I described.
I tried to Google the matter, but it is hard to do when I do not know exactly what to search.
I would appreciate any guideline about the problem.