We have recently been trying to use the zero-one inflated beta regression macros that were published as part of SAS Global Forum 2012 (see... https://support.sas.com/resources/papers/proceedings12/325-2012.pdf they have been very helpful in more accurately modelling the Loss Given Default (LGD) data that we encounter.

I am puzzled by something we have observed, and was hoping someone might have some suggestions as to how to proceed.

In our dataset, the probability of the zero and one outcomes are ~55% and ~22% respectively. After modelling, the average model-predicted probabilities are ~71% and ~48% respectively. I am surprised that: (i) the predicted probabilities appear to be biased (i.e., they are not replicating the observed probabilities in the dataset); and (ii) that they sum to >100%, which is obviously impossible.

We want to use the model to make forecasts, but given the 'raw' predictions this would obviously result in overestimation. I am thinking of making some simple adjustments (i.e., applying ratios of 55%/71% and 22%/48% to model predictions), but I am wondering whether someone might have some other suggestions that we should consider.

Appreciate any thoughts you might have.

I am puzzled by something we have observed, and was hoping someone might have some suggestions as to how to proceed.

In our dataset, the probability of the zero and one outcomes are ~55% and ~22% respectively. After modelling, the average model-predicted probabilities are ~71% and ~48% respectively. I am surprised that: (i) the predicted probabilities appear to be biased (i.e., they are not replicating the observed probabilities in the dataset); and (ii) that they sum to >100%, which is obviously impossible.

We want to use the model to make forecasts, but given the 'raw' predictions this would obviously result in overestimation. I am thinking of making some simple adjustments (i.e., applying ratios of 55%/71% and 22%/48% to model predictions), but I am wondering whether someone might have some other suggestions that we should consider.

Appreciate any thoughts you might have.

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