What kind of data sets can be helpful for predictions? One category of data that often are ignored are the data that you can get by simply asking people – or, as it is sometimes called, “walking about”. In a recent article about predicting unemployment, the authors note that this kind of often qualitative data can be very efficient in forecasting, and that even if you just ask people if they are worried about something that index can be useful to understand:
However, in Blanchflower and Bryson (2021) we indicate that fear of unemployment predicts both – that is, it can predict the real GDP change (post revisions) and unemployment change. We know, therefore, that things are getting bad when the fear numbers turn seriously negative in consecutive months. It is this – the turning point in fear of unemployment – that should really be the focus of our modelling efforts if we are to predict turning points in the business cycle.
The potential for further forecasting research using these kinds of data seems high.
Bryson A & Blancheflower, D “How the economics of walking about helps predict unemployment”VoxEU 24th August 2021
The insight here – that aggregated, deeply subjective views catch deeper trends – seems right to me, and somewhat underutilized. It also seems to indicate that the best indices in complex feedback systems with independent agents may be the sentiments of the agents themselves.