Following my first ‘Speakonomics’ on Game Theory a few months ago, this second one unpacks a phrase heard frequently this week – the ‘Fed’s Dot Plot’. I was asked by someone last evening, after a forum on the economy, “what is this dot plot?”.
The ‘Dot Plot’ is something that features in the Federal Open Market Committee (FOMC) decision on interest rates, along with the minutes of the meeting. Essentially what it is is a representation of the Fed officials’ (the folks part of the FOMC meeting) outlook on rates at a point in time. Put differently, it is the plot of what FOMC participants feel should be the appropriate federal funds rate at the end of the year; essentially charting the appropriate monetary policy path.
The background is that the US Federal Reserve (“the Fed”), as widely expected, raised rates on Wednesday by 25 percentage points, ending almost a decade long period of near-zero interest rates. Much of it is likely to have already been priced in. But how the Fed proceeds in its tightening cycle will be what we need to watch next year.
This is what the latest ‘Fed dot plot’, alongside the rate revision this week, looked like. The next edition – the update to this plot – will only be in its next meeting in March next year.
As the Fed explains:
This chart is based on policymakers’ assessments of appropriate monetary policy, which, by definition, is the future path of policy that each participant deems most likely to foster outcomes for economic activity and inflation that best satisfy his or her interpretation of the Federal Reserve’s dual objectives of maximum employment and stable prices.
Each shaded circle indicates the value (rounded to the nearest 1⁄8 percentage point) of an individual participant’s judgment of the midpoint of the appropriate target range for the federal funds rate or the appropriate target level for the federal funds rate at the end of the specified calendar year or over the longer run
In general, ‘dot plots’ are a widely used tool for statistical representation, and particularly useful for assessing distributions when there is a relatively small amount of observations.