Normally, your forecasts are generated by some model. That model is usually fitted to historical data and then used to produce forecasts. If the forecasts are bad, you need to fix the model, not overlay another model on top of it.
The forecasts may be coming from some external agency, not from me (for instance, they may be consensus forecasts generated by a poll). My goal is to use that number and come up with a better forecast from it. I could frame it as changing the model, but since I don’t control the publication of the original forecast, I’m conceptualizing it as coming up with a new forecast.
Many forecasts have been observed to have systematic biases of some sorts so that explicitly correcting for these biases gives more accurate forecasts. After I published the post, I came across the name for using linear regression for improving forecasts. It’s called Theil’s correction. If you’re controlling the forecast yourself, you can apply that before you publish the forecast. If the publication of the forecast is outside of your control, however, you need to apply that forecast afterwards.
The forecasts may be coming from some external agency, not from me… My goal is to use that number and come up with a better forecast from it.
In this case calling your inputs “forecasts” is just confusing. For you they are nothing but data on the basis of which you will build your own model to produce your forecasts.
In this framework you’re just doing normal forecasting and should use all the normal tools. There’s no reason to limit yourself to OLS regression, for example.
OLS regression isn’t the only tool, but it is the most standard one to fit a functional form. One can use other kinds of regressions. My focus was on techniques that can use existing forecast estimates in a black-box fashion rather than those that require one to create new models of one’s own on the evolution of the relevant processes.
but it is the most standard one to fit a functional form
It is the most simple one and probably the most widely used, though often inappropriately.
My focus was on techniques that can use existing forecast estimates in a black-box fashion rather than those that require one to create new models of one’s own on the evolution of the relevant processes.
As soon as you do something with “existing forecast estimates” other than just accepting them, you are creating a new model of your own. You want to correct them for bias? That’s a model you’ve created.
If you use external forecasts as data, as inputs, you are using them in “black-box fashion”.
The forecasts may be coming from some external agency, not from me (for instance, they may be consensus forecasts generated by a poll). My goal is to use that number and come up with a better forecast from it. I could frame it as changing the model, but since I don’t control the publication of the original forecast, I’m conceptualizing it as coming up with a new forecast.
Many forecasts have been observed to have systematic biases of some sorts so that explicitly correcting for these biases gives more accurate forecasts. After I published the post, I came across the name for using linear regression for improving forecasts. It’s called Theil’s correction. If you’re controlling the forecast yourself, you can apply that before you publish the forecast. If the publication of the forecast is outside of your control, however, you need to apply that forecast afterwards.
For an example of a paper that uses this sort of approach, see http://forecasters.org/ijf/journal-issue/489/article/7093
In this case calling your inputs “forecasts” is just confusing. For you they are nothing but data on the basis of which you will build your own model to produce your forecasts.
In this framework you’re just doing normal forecasting and should use all the normal tools. There’s no reason to limit yourself to OLS regression, for example.
OLS regression isn’t the only tool, but it is the most standard one to fit a functional form. One can use other kinds of regressions. My focus was on techniques that can use existing forecast estimates in a black-box fashion rather than those that require one to create new models of one’s own on the evolution of the relevant processes.
It is the most simple one and probably the most widely used, though often inappropriately.
As soon as you do something with “existing forecast estimates” other than just accepting them, you are creating a new model of your own. You want to correct them for bias? That’s a model you’ve created.
If you use external forecasts as data, as inputs, you are using them in “black-box fashion”.