But I disagree with Evan on how we should interpret such results. I took a look at the Survey of Professional forecaster data and restrict my analysis to the Great Moderation period since 1990. I also focus in on the 1 year forward NGDP forecast, as the 1 quarter forecasts give qualitatively similar results.
Like Evan, I too observe that current nominal GDP expectations are related to real variables, such as unemployment. However, the overall relationship is quite weak. NGDP expectations can only explain about 5% of the variation in unemployment. Moreover, the slope estimate is likely to be biased downwards as autocorrelation means the true slope is even closer to zero.
To control for this, we need to look not at the change in expectations, but rather changes in surprises. A big theme in nominal GDP disucssions is that nominal prices are sticky. Therefore, when NGDP falls below trend, because past nominal contracts were set under the expectation of the higher trend, markets fail to clear and we have a fall in real growth. Therefore, if this transmission channel were true, when NGDP falls below what was expected, we should expect to see a significant impact on unemployment.
In other words, we should consider whether actual nominal GDP hit forecasts or if it fell short. This way we can construct an error index that measures to what extent forecasters over or underestimated. Positive numbers denote when actual nominal GDP outperformed the forecast, and thus the dramatic fall into negative territory during the financial crisis reflects the unexpected nature of the nominal GDP shock.
While it certainly did fall during the financial crisis, if you use ordinary least squares regression on this index against variables of interest, such as unemployment, you will not find any kind of systematic correlation. However, if you consider only the extreme cases in which the forecast undershot reality by more than 4%, then you do get a significant negative correlation:
Perhaps this evidence suggests that expectations have a nonlinear impact on unemployment, but at that point we are drawing epicycles that the regression evidence does not warrant.
Does this all mean that expectations are useless? On the contrary. When investigating these expectation surveys, I did manage to uncover the following chart comparing forecasts and actual nominal GDP growth. I lagged the actual NGDP by 1 year, so it is easier to compare how the forecast compares with actual growth.
What we can see is that the forecasters, while not perfect, still do a rather good job of identifying times of distress. Given that forecasts do carry information, then this opens up a role for policy to lean against the wind. The Fed, instead of waiting for all the data to come in, could use a joint forecast-contemporary data criterion. If the forecasters are projecting slow future growth, then the Fed could announce that it is aware of a potential problem and prepare the necessary policy machinery, conventional or otherwise, to combat that threat.
Expectations matter, but we need to be clear on why. While they may have a direct impact on growth, expectations also serve as a crucial lens into the future and can carry information content for policy makers. Armed with such tools, monetary policy can turn towards the future, lean against the wind, and in doing so remedy demand shocks before they start to hurt.