#StackBounty: #regression #time-series #forecasting #residuals Predicting the Residuals of a Forecast Model

Bounty: 50

I have just read a paper [1] in which the authors try to forecast risk of some variable (earnings in this case) by deriving dispersion measures via forecasting quantiles of the respective variable, i.e. forecasted risk equals forecasted dispersion measures. They state that alternatively:

" […] one could capture conditional variance (dispersion) in future earnings by regressing the squared (or absolute) value of the residuals from an earnings forecasting model on predictor
variables"

To me, this is a completely new approach that seems to make only limited sense. If a set of predictor variables turns out to significantly impact the above-mentioned residuals, why not just include them in the original model? How does one "capture conditional variance", i.e. model the shape of the future distribution of a variable, via this approach?

Maybe this is a common practice, which I have not yet heard of. I would be grateful for any comments on this.

[1] Konstantinidi and Pope (2016) Forecasting Risk in Earnings, Contemporary Accounting Research Vol. 33 (2), pp. 487-525.


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