Pondering Robert Tetlow’s Unsolicited Advice for Kevin Warsh: A Bayesian Perspective on Forward Guidance, Transparency, and Monetary-Policy Credibility
Tetlow's framework appears to assume, at least implicitly, that the central bank operates within a reasonably stable and well-behaved probabilistic environment in which the reaction function can be identified, communicated, and subsequently inferred by market participants. A Bayesian perspective begins from a more demanding premise: the reaction function itself is conditional on the information set, the underlying economic model, the estimated parameters, and the distribution of risks—and all of these can change.
This distinction matters enormously.
The central question is not simply whether the Federal Reserve should communicate its reaction function. It is whether there is, at any particular moment, a sufficiently stable and identifiable reaction function for communication of that function to provide reliable guidance about future policy.
The distinction I would therefore draw is between forward guidance as commitment and communication as disclosure of the central bank's evolving posterior beliefs.
I. The Central Bayesian Objection
Tetlow's argument can be summarized approximately as:
Transparency → understanding of the reaction function → formation of market expectations → improved monetary-policy transmission.
Within a relatively stable monetary-policy environment, this is an entirely reasonable proposition.
But the proposition becomes considerably less secure when the central bank confronts structural breaks, geopolitical shocks, fiscal transformations, financial instability, nonlinear inflation dynamics, changing productivity, or uncertainty about the parameters of the monetary-policy transmission mechanism.
Under such circumstances, the reaction function should not be regarded as a fixed object waiting to be discovered by financial markets.
A Bayesian central bank is continuously updating its beliefs about the economy.
In simplified notation, its evolving assessment can be represented as:
P(θ | It)
where θ represents the uncertain characteristics of the underlying economic structure and It represents the information available at time t.
The appropriate policy instrument therefore depends not merely on observed inflation or unemployment, but on the central bank's evolving assessment of the economic structure, risks, and uncertainty surrounding its models. In simplified form:
it = f[Et(θ | It), Rt, Ωt]
where:
it = the policy interest rate;
Et(θ | It) = the central bank's updated assessment of the economic structure;
Rt = the relevant distribution of risks; and
Ωt = uncertainty surrounding the model and its parameters.
The implication is profound:
A central bank should not be expected to remain faithful to yesterday's reaction function merely because yesterday's communication caused markets to form expectations around it.
Indeed, doing so can invert the proper relationship between information and policy.
Policy should respond to information. Information should not be subordinated to the preservation of a previously communicated policy path.
II. Forward Guidance: Commitment versus Conditional Belief
Tetlow provides a useful distinction between Odyssean and Delphic forward guidance. Odyssean guidance involves a commitment that can constrain future policy, while Delphic guidance communicates the central bank's economic outlook and the associated likely policy path.
This distinction is important, but a Bayesian interpretation takes the argument one step further.
The fundamental problem with strong forward guidance is not merely that the forecast may turn out to be wrong. Forecast errors are unavoidable. The deeper problem is that a sufficiently credible commitment can alter the policymaker's incentives after new information arrives.
Suppose a central bank communicates in January:
Given the information available today, we expect the policy rate to follow path X.
By April, however, new information materially changes the probability distribution over possible economic states.
A Bayesian policymaker should then be able to say:
The information has changed; our assessment has changed; therefore our policy has changed.
If instead the policymaker concludes:
We must continue toward X because we previously told financial markets that we would,
then forward guidance has ceased to be merely a communication device. It has become a constraint on Bayesian learning.
That is not necessarily credibility.
It can become institutional inertia disguised as credibility.
This is precisely where Christine Lagarde's observation at Sintra becomes particularly interesting. As Tetlow notes, Lagarde expressed regret that she had at one point felt "bound" or "compelled" by forward guidance.
From the conventional perspective, the lesson is that Odyssean forward guidance can tie the hands of policymakers.
From the Bayesian perspective, the lesson is deeper:
A commitment can increase the cost of responding rationally to new information.
Once markets have incorporated a policy commitment into asset prices, reversing course can become costly. The central bank may consequently find itself caught between two objectives:
New information → optimal policy may change
while simultaneously:
Previous guidance → cost of changing policy increases.
The institution can then become trapped between economic optimality and reputational consistency.
For a Bayesian policymaker, that is precisely the danger.
III. Does Tetlow Conflate Credibility with Consistency?
This is perhaps the most important conceptual difference between Tetlow's framework and the Bayesian perspective developed here.
Tetlow is rightly concerned that an abrupt retreat from forward guidance could weaken transparency and impair the monetary-policy transmission mechanism. But there are at least two different meanings of credibility.
Procedural credibility
The central bank does what it previously said it would do.
Epistemic credibility
The central bank changes what it does when sufficiently strong evidence changes its assessment—and clearly explains why.
These two forms of credibility are not necessarily identical.
Indeed, under conditions of radical uncertainty, epistemic credibility may be more valuable.
A central bank that rigidly follows an obsolete forecast merely to preserve consistency may appear predictable, but it may simultaneously lose credibility as a competent economic institution.
Conversely, a central bank that changes course whenever the latest data point changes would also be behaving badly.
Bayesian updating is not data-point chasing.
The relevant question is whether the weight of evidence has changed sufficiently to alter the posterior distribution of beliefs.
The Bayesian central banker therefore asks:
How reliable is the new information?
Is it persistent or transitory?
Is it consistent with other evidence?
Does it alter the estimated parameters of the model?
Does it support one model over another?
Has the probability of a major regime change increased?
Has the balance of risks materially shifted?
This is very different from mechanically reacting to each incoming statistic.
IV. The Reaction Function Is Not Necessarily a Stable Object
Tetlow's proposed alternative to forward guidance is particularly interesting. If the Federal Reserve does not wish to commit itself to a future policy path, it can explain its reaction function sufficiently well that markets can infer future policy from incoming economic data.
The idea is elegant.
But it rests on an important assumption:
that the reaction function is sufficiently stable and identifiable for markets to infer it.
That assumption deserves considerably more scrutiny.
A conventional Taylor-type reaction function can be represented as:
it = r + πt + α(πt − π) + β(yt − y*t)**
where:
it is the policy interest rate;
r* is the equilibrium real interest rate;
πt is inflation;
π* is the inflation objective;
yt − yt* is the output gap; and
α and β represent the policy response to inflation and economic slack.
But almost every important variable in this equation raises a Bayesian question.
What is the true value of r*?
What is potential output?
What is the output gap?
What is the underlying Phillips curve?
How persistent is inflation?
Has the monetary transmission mechanism changed?
Has fiscal policy altered the equilibrium relationship?
Has productivity shifted?
How should geopolitical shocks be incorporated?
Has the relationship between unemployment and inflation changed?
These are not simply observed quantities.
They are latent variables, uncertain parameters, and model-dependent estimates.
Consequently, markets cannot simply "learn" the Federal Reserve's reaction function from incoming data as if they were observing a stable mechanical rule.
The data themselves must first be interpreted.
And interpretation requires a model.
V. From Parameter Uncertainty to Model Uncertainty
This is where the Bayesian argument becomes more consequential.
In a conventional framework, uncertainty is often represented as uncertainty about the values of parameters within an assumed model.
A more genuinely Bayesian approach also allows uncertainty about the models themselves.
Instead of asking only:
What are the values of θ within model M?
the central bank must sometimes ask:
Which model M is more plausible in the first place?
The relevant posterior can therefore be represented as:
P(Mj, θj | It)
where Mj represents competing models and θj represents their associated parameters.
The central bank is therefore updating not merely its estimates of economic parameters but potentially the probability assigned to competing representations of the economy itself.
This is a substantially more difficult problem.
It also explains why a simple reaction function can be useful as a benchmark without being an adequate description of actual monetary-policy decision-making.
Tetlow himself acknowledges an important qualification in his footnote: central banks with which he is familiar generally use simple monetary-policy rules as guides rather than mechanically adhering to them.
That observation is important because it actually brings his position closer to the Bayesian perspective.
If the reaction function is ultimately a guide rather than a binding rule, then the crucial issue becomes how policymakers update their judgment when the assumptions underlying that guide change.
VI. Transparency Is Not the Same as Revealing a Mechanical Rule
Tetlow argues, persuasively, that transparency is an important obligation of an independent central bank. I agree with the democratic and institutional logic of this argument.
But I would qualify the proposition that transparency necessarily requires the communication of a stable reaction function.
There is an important distinction between:
transparency about reasoning
and
transparency about a predetermined policy rule.
A Bayesian central bank can be highly transparent while explicitly acknowledging that it cannot provide a reliable mechanical rule for the future because the probability distribution over economic states is changing.
Indeed, such communication may sometimes be more intellectually honest than presenting a highly precise reaction function that conveys a false impression of certainty.
The central bank can explain:
what it currently believes;
which evidence has caused that assessment;
how its assessment differs from the previous assessment;
which risks could cause another revision;
which indicators it is monitoring;
how policy would respond under alternative economic states.
This is not opacity.
It is conditional transparency.
And conditional transparency may be particularly appropriate in a world characterized by structural change and radical uncertainty.
VII. The "Vacuum" Argument
Tetlow argues that if the Federal Reserve does not communicate, somebody else will fill the informational vacuum. This is a reasonable concern.
Financial markets will inevitably form expectations.
But the existence of market expectations does not establish that the central bank should attempt to control those expectations through detailed forward guidance.
The deeper question is:
Should the central bank tell markets what it expects to do, or should it teach markets how it learns?
The Bayesian answer is the latter.
The central bank should not attempt to tell markets what the future will be.
It should explain how its beliefs about the future are formed and how new information can change those beliefs.
The difference can be expressed simply.
Conventional forward guidance
"We expect rates to be X."
Rule-based guidance
"Rates will respond according to rule Y."
Bayesian conditional guidance
"Our current assessment assigns the greatest probability to state A. If incoming evidence materially increases the probability of state B, our policy response will change accordingly."
The third approach preserves flexibility without sacrificing transparency.
It also provides markets with something potentially more valuable than a forecast:
an understanding of the architecture of the central bank's learning process.
VIII. Where Tetlow's Criticism of Warsh Is Persuasive
The Bayesian critique of Tetlow should not be mistaken for an unconditional defense of Kevin Warsh.
Tetlow identifies an important issue when he argues that rejecting forward guidance should not mean abandoning communication about contemporaneous policy decisions.
I agree.
Rejecting forward guidance does not logically imply rejecting explanation.
If Warsh refuses to explain the reasoning behind a current policy decision, then Tetlow's criticism has force.
A Bayesian central banker should arguably be more communicative about the information process, not less.
The appropriate distinction is therefore:
Forward guidance
"We intend to do X in the future."
Bayesian transparency
"Given today's information, we assign particular probabilities to competing economic states. This assessment explains today's decision. New information can alter those probabilities and therefore alter our policy."
The latter may provide markets with something more valuable than a rigid forecast:
an understanding of the policy-learning process.
This is the area in which Tetlow's warning deserves to be taken seriously.
Warsh should not confuse skepticism toward forward guidance with skepticism toward accountability.
The two are entirely different propositions.
IX. Commitment, Time Consistency, and the Limits of the Bayesian Critique
There is, however, an important qualification.
It would be too strong to argue that a central banker should always abandon previous guidance when new information arrives.
There are circumstances in which maintaining a commitment despite new information can be rational.
Commitment can help solve a time-consistency problem. It can influence expectations, affect credibility, and produce economic benefits that would not arise under pure discretion.
The Bayesian argument therefore should not be reduced to:
"Always abandon forward guidance when new information arrives."
That would be as simplistic as the rigid guidance it criticizes.
The more defensible proposition is:
A central banker who treats previous forward guidance as binding even when sufficiently powerful new evidence has materially changed the posterior distribution is failing to distinguish commitment from evidence-based policy.
That statement is considerably stronger theoretically because it recognizes that commitment itself can have value.
And the converse is equally important:
A central banker who changes policy whenever the latest data point changes is also not practicing Bayesian policy.
Bayesian policy lies between dogmatism and data-point opportunism.
It requires:
commitment when commitment has genuine economic value;
flexibility when the information set materially changes the posterior distribution;
and
transparency about why the posterior has changed.
X. Two Conceptions of Monetary-Policy Credibility
The disagreement between Tetlow's perspective and the Bayesian perspective can ultimately be expressed as two different conceptions of credibility.
The conventional framework
Stable reaction function → transparent communication → predictable expectations → effective transmission.
This framework emphasizes predictability.
The Bayesian-adaptive framework
Information → belief updating → reassessment of models and risks → conditional policy → transparent explanation of the update.
This framework emphasizes epistemic adaptability.
Under ordinary conditions, the two frameworks can coexist.
Indeed, there is no reason why they should be regarded as mutually exclusive.
But under radical uncertainty, structural change, or model instability, they can diverge sharply.
The central difference is therefore not whether the central bank should be transparent.
It is what it should be transparent about.
The conventional approach emphasizes the policy reaction function.
The Bayesian approach emphasizes the information, beliefs, risks, and updating process that generate the policy decision.
XI. The Deeper Epistemological Issue
The most consequential difference may therefore lie in the treatment of uncertainty itself.
Tetlow's argument appears to treat uncertainty principally as uncertainty about the future values of economic variables.
A Bayesian central banker must confront a deeper problem:
uncertainty about the model generating those variables.
This distinction is fundamental.
Suppose inflation unexpectedly rises.
A conventional interpretation might ask:
How should the reaction function respond to higher inflation?
A Bayesian interpretation asks several questions first:
Is the inflation increase persistent?
Is it demand-driven or supply-driven?
Has the Phillips curve changed?
Has inflation expectations changed?
Has productivity changed?
Is this a temporary geopolitical shock?
Is the economy entering a different regime?
Has the estimated natural rate changed?
Is the historical model still reliable?
Only after those questions are considered does it make sense to determine the appropriate policy response.
Thus, the policy rule is downstream from the process of inference.
This is the central epistemological point that, in my view, is missing from much of the conventional debate over forward guidance.
XII. From Forward Guidance to Bayesian Conditional Guidance
The real alternative to conventional forward guidance is therefore not silence.
Nor is it a mechanical Taylor rule.
Nor is it simply a refusal to communicate.
The more promising alternative is what I would call Bayesian conditional guidance.
Under this approach, the central bank communicates four things:
First, its current assessment of the economy.
Second, the distribution of risks surrounding that assessment.
Third, the evidence that would cause it to revise its assessment.
Fourth, the broad policy implications of alternative states of the world.
This creates a communication framework that is simultaneously transparent and adaptive.
It tells markets:
We are not promising you a predetermined future.
But it also tells them:
We are not acting arbitrarily.
Instead:
Here is what we currently believe, here is why we believe it, here is what could change our minds, and here is how policy would respond if it did.
That may be the most credible form of communication available under radical uncertainty.
XIII. A Bayesian Defense of Warsh—With an Important Caveat
From this perspective, Warsh's skepticism toward conventional forward guidance is intellectually defensible.
Indeed, the historical experience cited by Tetlow himself demonstrates why policymakers can become uncomfortable with guidance that later constrains their ability to respond to changing circumstances. His discussion of the Federal Reserve's reluctance to invoke an escape clause during the inflation surge of 2021 illustrates the potential cost of excessive commitment.
But Warsh should be careful.
The rejection of forward guidance should not become a rejection of epistemic transparency.
A central banker does not need to tell markets precisely where interest rates will be six months from now.
But the central banker should explain:
what the FOMC currently believes;
what evidence supports that belief;
what uncertainties surround it;
which developments could change it; and
how policy would respond to materially different states of the economy.
That is not forward guidance in the conventional sense.
It is transparent Bayesian conditionality.
XIV. Conclusion: Credibility Through Learning, Not Through Inertia
Robert Tetlow's essay is a valuable contribution to the debate over Federal Reserve communication. His distinction between Odyssean and Delphic forward guidance is useful, and his insistence that rejecting forward guidance should not become an excuse for abandoning transparency is entirely reasonable.
Where I respectfully part company with Bob is in the implicit epistemological architecture underlying the argument.
The monetary-policy reaction function is not necessarily a fixed object that markets can simply learn.
It is conditional on the central bank's information set, its model of the economy, its estimates of latent variables, and its assessment of risks. Those beliefs can change. The models can change. The parameters can change. And, in periods of structural transformation, the probability assigned to competing models can change.
Consequently, a serious central banker should never regard previous forward guidance as an obligation to ignore sufficiently powerful new Bayesian evidence.
Doing so would confuse credibility with consistency.
The credibility of an independent central bank should rest on something deeper:
A central bank's credibility should not derive from its willingness to remain faithful to forecasts that have been invalidated by new evidence. Its credibility should derive from demonstrating that it can distinguish commitment from dogmatism, update its beliefs when the evidence warrants it, and explain those updates transparently to the public and to financial markets.
This does not mean that every new data point should produce a policy reversal. Bayesian updating is neither mechanical nor instantaneous. Evidence must be evaluated according to its reliability, persistence, relevance, and consistency with competing explanations.
Nor does it mean that commitment has no value. On the contrary, commitment can be economically valuable when it solves a time-consistency problem or stabilizes expectations.
The appropriate principle is therefore neither "always commit" nor "always adapt."
It is:
Commit when commitment has economic value; adapt when sufficiently strong evidence changes the posterior distribution; and explain transparently why the assessment has changed.
In this sense, the true alternative to forward guidance is neither silence nor a mechanical reaction function.
It is Bayesian conditional guidance:
Communicate the information set, the distribution of risks, the current posterior assessment, and the conditions under which that assessment would change.
This approach reconciles the legitimate institutional demand for transparency emphasized by Tetlow with the epistemic flexibility emphasized by Warsh.
Most importantly, it recognizes a fundamental reality of modern monetary policymaking:
The central bank does not merely respond to the economy. It is continuously learning about the economy.
And when the learning changes, policy must retain the freedom to change with it.
That is not a failure of credibility.
Under genuine uncertainty, it may be the highest form of credibility.