Don't look back in hangar steak

I'm currently in the queue for Oasis tickets. Rather than mindlessly watching the counter of people in the queue ahead of me fall (currently 184,984 people), I thought I'd start writing a blog article. Unfortunately, attempting to write a blog whilst hungry is perhaps a suboptimal approach for mustering a bit of creativity. So instead my thoughts have gone towards lunch, a burger, maybe a hanger steak, or what about rib eye steak. Ultimately, all these things are derived from different cuts of beef. If you ordered a burger and got a steak, you might be somewhat confused, right, even if ultimately they are all made of beef. It's not only that the cuts are different, they are also likely to be cooked and prepared in very different ways as well.

It's kind of like that when it comes to data. You might have precisely the same dataset, but precisely how you slice and dice it will give you very different results. The first thing to consider is how you judge what a "good" dataset is? Let's think about macroeconomic forecasting data, of the type that Turnleaf Analytics produces, primary for inflation, but also various growth proxies like ISM manufacturing. The simplest way to assess how "good" this forecast dataset is to calculate the absolute mean error, between our forecast and where the actual number ends up in the future. You can do the same thing with other forecast sources, and then compare, and pick the best one.

In practice, you might actually combine our forecasts, with other ones, such as your own forecast. For example, if both sets forecasts you are examining are quite accurate, and the errors are not correlated, you will likely observe that their combination produces an even smaller error. Furthermore, we need to consider that it is likely that we will combine not only different forecasts together, but many different types of datasets. This combination of datasets is likely to give us different and likely better results, compared to looking at one dataset in isolation. Given there are so many datasets out there, it is likely that the combination of datasets selected can differ significantly from team to team. On top of that the data analysis which will be performed will likely be somewhat different...

To view the rest of the blog please click at the following link on the Turnleaf Analytics website!