......the history of economic forecasting tells us that our central forecast will almost certainly be wrong. But there are things we can do to manage this uncertainty.
The methodology in Philip Tetlock's Superforecasting is very helpful: try, fail, analyse, adjust, try again.(Tetlock P and D Gardner (2015), Superforecasting: The Art and Science of Prediction, Crown Publishing, New York).
It is essential to ask, after the fact, what did cause our forecasts to be wrong? Evaluating forecasts ex post is as important as generating the forecasts. This can be described in three stages:
Where were we wrong? For which variables were our forecast misses the largest?
Why were we wrong? There are a number of possible reasons. Was it because the model was the wrong model? Has the model changed? Was our judgemental adjustment wrong? Was our forecast for an explanatory variable wrong? Was there an economic event or ‘shock’ that we didn't anticipate?
Having attempted to answer these questions, we can then ask what can we learn? What, if anything, do we need to adjust in our forecasting framework?Useful speech by Australian Reserve Bank Deputy Governor Guy Debelle on the problem of uncertainty in economic forecasting and the development of monetary policy. He begins: "Uncertainty is one of the few certainties in monetary policy decision-making. It enters at nearly every stage of the process – from understanding where the economy is at the moment to knowing where it will be in the future" From that point, he discusses some of the main ways that uncertainty affects things along with the nature of the Bank's responses. It's a simple and useful speech.
Guy Debelle, Uncertainty, 26 October 2017
The problems of uncertainty are not limited to forecasting nor to macro-economic policy. They bedevil all policy making. Problems here have risen exponentially with the rise of measurement, key performance indicators and "evidence based" public policy. Policy has become locked into a strait jacket set largely by what can be easily measured in circumstances where available statistics are often scanty, lagged and with uncertain meaning. The result is policy failure on a large scale.