Inflation forecasts or irrational exuberance?

If your eyes glaze over at the mention of inflation, take a look at this and feel free to go:

My own calculations using forecast figures from the SMP and inflation data from the ABS

Forecasting inflation

Forecasting is an unforgiving art. There are an infinite number of wrong answers, and one right one. The years since the GFC have been particularly unforgiving for central bank forecasts, and no forecast has struggled so much as inflation. First too high, now too low, inflation has a mind of its own which central banks have yet to fully discern.

No surprise then that central banks have grown more cautious. They have commissioned research into their forecasting track record to better understand the scope of the problem, and what might be done about it (RBA, RBNZ, and BoE). Forecasts are now accompanied by caveats, confidence intervals, and cautious language.

An example of representing uncertainty better (Reserve Bank of Australia Statement on Monetary Policy Feb 2015)

While helpful, there are still two small issues:

The reports I’ve read primarily assess forecasts for accuracy. This involves calculating the difference between the forecast and reality and adjusting it to remove the sign.* This shows the error’s magnitude – the higher the number, the greater the deviation – but not the direction of the forecasts or actual inflation. Some reports do also test for bias – the degree to which results repeatedly skew in one direction – but not all.

Second, most of the reports are from before 2015, around the time when problems with inflation started to appear.

I want to see recent results, so I’ve started building a data set from publicly available forecasts. I will eventually do this for all the major central banks, but started with Australia because it has enjoyed uninterrupted growth for the period when the rest of the world has been in and out of crisis. This should minimise forecasting errors from big shocks like Trump, Brexit, or the Euro-crisis.

Persistent errors in the same direction

Inflation forecasts between 2005 and 2014 missed sudden changes in the inflation rate, like in 2006, 2011 or 2012. Some of these mistakes were over-estimations, others were under-estimations. The errors are not surprising given the global recession, and lots of fiscal and monetary stimulus going around.

My own calculations using the figures provided by the RBA
My own calculations using the figures provided by the RBA

2015 is where it gets interesting. Inflation forecasts have been persistently panglossian over the last five years; forecasts keep reaching for the sky while inflation trundles along below. From 2017 the forecasts sober up a little, but the errors are still all in the same (hopeful) direction.

My own calculations using forecast figures from the SMP and inflation data from the ABS

When the forecast errors trend in the same direction, one of the relationships in the model may be misrepresented.*** This kind of diagram illustrates the issue in a way that accuracy bar charts do not. It also suggests that forecasters may not have updated the model.

Forecasting macroeconomic variables may be the only form of divination that requires a suit, but forecasts can still be useful. We feel more comfortable about an uncertain future if we can attach numbers and neat lines to it. This psychological comfort helps us make decisions about the future, whether to buy a house, or invest in a plant that manufactures fidget spinners. If these beliefs are shared by enough people, they can become self-sustaining, and even ‘true’ by simple fact that everyone now believes them. Forecasts can also give a reliable sense of direction,

If the past has any bearing on the future, forecasts can also calibrate models for economic or social behavior. In practice, these models can often give us a reliable sense of direction, if not complete certainty. Here, mistakes are useful for tuning the machine, but not all mistakes are made equal. One-off shocks, like the GFC, add little to models that, by design, cannot predict the unpredictable. Recurrent forecasting errors are another matter. They may be a signal. Taken in this way, the errors are not so much the inevitable failures that go with any attempt to peer past the veil of the present, but indicators of a more persistent change underway.

Or maybe not. It’s the future after all.


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*Called a root-mean-square-error (RMSE)

** Central banks use a larger battery of statistical tools than the simple RMSE

***The Philips Curve is flattening

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