Fed Confused Reality Doesn't Conform To Its Economic Models, Shocked Its Models Predict "Explosive Inflation"

Below are several excerpts only the brains of those practicing the world's most useless profession (and we are very generous with that assessment) could possibly come up with, in attempting to explain the shocking outcome of reality continuously refusing to comply with their exhaustive and comprehensive Dynamic Stochastic General Equilibrium (DGSE - don't worry: it sounds complicated - it must be very serious and important, and be thus very credible and good at predicting stuff; it is neither) models.

With short-term interest rates at the zero lower bound, forward guidance has become a key tool for central bankers, and yet we know little about its effectiveness. Standard medium-scale DSGE models tend to grossly overestimate the impact of forward guidance on the macroeconomy—a phenomenon we call the “forward guidance puzzle.”
New Keynesian DSGE models following the work of Christiano et al. (2005) and Smets and Wouters (2007) are in principle well suited to study the effects of forward guidance
While the literature has provided strong theoretical justications for the use of such forward guidance (e.g., Eggertsson and Woodford (2003)), the evidence on the quantitative effects of such a policy tool on the macroeconomy is scarce
Empirically, Gurkaynak et al. (2005) and more recently Campbell et al. (2012) found strong evidence that FOMC announcements move asset prices. Yet when Campbell et al. try to assess the impact of exogenous anticipated changes in monetary policy on the macroeconomy, they find that this has the opposite sign than expected, highlighting these identication challenges.
Given that policymakers seldom if ever experimented with forward guidance this far in the future, there is little data to guide them.
The problem, however, is that these DSGE models appear to deliver unreasonably large responses of key macroeconomic variables to central bank announcements about future interest rates (a phenomenon we can call the "forward guidance puzzle")

It gets better:

In general it will always be hard, if not impossible, to test the predictions of DSGE models by looking at the outcome of policy counterfactuals such as the ones in our paper: even if the counterfactual is implemented, this will not occur in a controlled environment.
Nonetheless, we argue that counterfactuals like the one performed here are useful for policy makers in order to quantify the potential eects of their policies, particularly when alternative approaches are lacking as is the case here.
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