A Sequential Policy-Rate Diagnostic: Monetary Absorption and the Inflation Floor Safeguard
This paper introduces the Monetary-Absorption Rule with a Common-Inflation Floor and Employment Safeguard (MAR-IFS), a sequential policy framework that utilizes a max operator to prevent the offsetting or mechanical accumulation of conflicting economic signals, thereby offering a distinct alternative to additive rules like the Taylor rule for determining interest rates.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Central banks act as the thermostat for a nation's economy, adjusting the cost of borrowing money to keep prices stable and jobs plentiful. When the economy runs too hot, they raise rates to cool things down; when it runs too cold, they lower rates to warm it up. But deciding exactly when to turn the dial, and by how much, is rarely simple. Economists often look at different signals to make this call: how fast prices are rising, how much money is circulating in the banking system, and how many people are out of work. The challenge arises when these signals disagree. If prices are climbing but the money supply is shrinking, or if unemployment is high while inflation is stubborn, the standard tools for combining these clues can produce confusing or contradictory advice. This is the territory explored by a new study from Georgetown University, which proposes a different way to listen to the economy's conflicting voices.
The researcher, Masaaki Yoshimori, introduces a method called the Monetary-Absorption Rule with a Common-Inflation Floor and Employment Safeguard. To understand what this does, imagine a central bank trying to decide on a single interest rate. Traditionally, many models simply add up the pressure from inflation and the pressure from the money supply. If both are high, the model adds them together to suggest a very high rate. If one is high and the other is low, the model might let the low one cancel out the high one, suggesting a moderate rate. Yoshimori argues that this "adding up" approach has a flaw. It can either double-count the same economic pressure if the signals overlap, or it can let a weak signal dilute a strong one when they point in different directions. Instead of adding the signals, his new rule asks a simpler question: which signal is the strongest right now, and does it demand action?
The study builds a diagnostic tool that looks at three specific things. First, it measures "monetary absorption," which tracks how much extra money has piled up in the system relative to how fast that money is actually moving through the economy. Think of it as checking if there is a flood of cash waiting to be spent. Second, it looks at a "common inflation" measure. Rather than relying on just one official price index, which might be skewed by volatile items like food or energy, this rule averages four major price measures to get a clear picture of broad, underlying price trends. Third, it includes a safeguard for employment, which only kicks in if unemployment rises significantly above its natural level, acting as a brake to prevent the rule from being too harsh on a struggling job market.
The core innovation is how these three pieces are combined. Instead of adding them, the rule uses a "maximum" logic. It compares the pressure from the money supply against the pressure from inflation and picks the higher of the two. If inflation is screaming for higher rates but the money supply is quiet, the rule listens to inflation. If the money supply is screaming but inflation is calm, it listens to the money. This prevents a quiet signal from muting a loud one. It also prevents the rule from accidentally stacking two signals that are actually saying the same thing, which could lead to an unnecessarily aggressive rate hike. The rule then applies the employment safeguard only if the job market is truly weak, ensuring the final recommendation is not overly restrictive when people are losing their jobs.
To test this idea, the researcher ran a simulation using historical data from 2006 through mid-2026. The goal was to see what interest rate this new rule would have suggested at every point in time, and how that compared to the actual rates set by the Federal Reserve and to other standard models. The results showed that all the models, including the new one, agreed on a major turning point: by late 2021, the economy had shifted enough that interest rates needed to rise significantly to normalize. All the models signaled that rates should cross the 3 percent mark in the fourth quarter of 2021 and the 4 percent mark in early 2022. This agreement suggests that the timing of the shift was clear across different ways of looking at the data.
Where the models diverged was in the intensity of the response. When the signals for inflation and money both pointed toward raising rates, the standard "additive" models suggested rates would climb much higher, reaching nearly 8.3 percent at their peak. The new rule, by picking the stronger signal rather than adding them, suggested a peak of about 6.7 percent. This difference is substantial; it is equivalent to roughly eight standard interest rate increases. Conversely, when the signals later began to pull in opposite directions, the additive models allowed the weak signal to cancel out part of the strong one, suggesting a lower rate. The new rule, refusing to let the weak signal dilute the strong one, maintained a higher prescription. This demonstrates that the way you combine the information changes the final number, even if the information itself is the same.
Looking at the most recent data in the simulation, which extends to the second quarter of 2026, the new rule suggests a current interest rate of roughly 3.95 percent if it focuses on the most recent monthly inflation data. However, if it looks at inflation trends over the past year or two, the suggested rate settles closer to 3.6 percent. This range aligns closely with the actual federal funds rate observed in the simulation, which was 3.63 percent. The study notes that the new rule does not necessarily predict the future better than existing models; in fact, a standard model known as the Taylor rule tracked historical rates slightly better over the entire period. The value of this new approach is not that it is a perfect crystal ball, but that it acts as a transparent monitor. It tells policymakers and observers exactly which economic force is driving the recommendation at any given moment.
The study concludes that the method of combining economic signals is not just a technical detail but a fundamental choice that shapes policy. By refusing to let a weak signal mute a strong one, and by avoiding the mechanical stacking of overlapping pressures, this sequential rule offers a clearer view of what the economy is actually demanding. It provides a way to distinguish whether a call for higher rates comes from a surge in prices or from an excess of money in the system. In a world where economic signals often conflict, this clarity helps ensure that the decision to turn the thermostat is based on the loudest, most urgent voice, rather than a muddled average of all the noise.
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