Financial columnists love a predictable ritual. Every time inflation prints higher than expected or crude moves five dollars in either direction, the predictable critiques emerge: central bank forecasting models are broken, the Bank of England is relying on outdated dynamic stochastic general equilibrium (DSGE) frameworks, and Threadneedle Street is blind to the realities of modern energy markets.
The conventional narrative argues that if central banks just built more granular, hyper-complex models that account for every geopolitical ripple in the Strait of Hormuz, monetary policy would magically stabilize.
This view is entirely wrong.
The lazy consensus misses the foundational mechanics of how macroprudential policy interacts with structural economic shifts. I have spent years evaluating how major financial institutions allocate capital against regulatory stress scenarios. I have watched risk departments burn through millions trying to build the "perfect" predictive energy model. It is a fool’s errand.
The demands for hyper-accurate oil shock modeling ignore a fundamental truth of monetary economics: central bank models are not designed to be weather forecasts. They are designed to be structural guardrails. Demanding that the Bank of England precisely map the micro-transmission of an oil price spike to consumer baskets is asking a lighthouse to predict the exact height of the next wave.
The Fallacy of the Granular Energy Model
Commentators argue that the Bank of England's modeling failed because it did not anticipate the prolonged structural shifts in energy distribution channels over the last few years. They want the bank to replace its aggregate supply-shock variables with granular, real-time data inputs.
This reveals a profound misunderstanding of econometric modeling. When you introduce hyper-granularity into a macroeconomic framework, you do not increase accuracy; you increase parameter uncertainty. In econometric modeling, this is known as the over-fitting trap.
Historically, oil price shocks were treated as pure exogenous supply constraints (Kilian, 2009). A pipeline shuts down, supply drops, prices rise, and inflation follows. However, modern research shows that oil price shocks are driven by a complex mix of global demand adjustments, precautionary inventory hoarding, and financialization (Kilian, 2009).
[Exogenous Shock View] Supply Disruption ───> Higher Prices ───> Inflation
[Modern Financial View] Global Demand + Liquidity Shifts ───> Financial Accelerator ───> Systemic Risk
If a central bank attempts to build a structural model that accounts for every variable—ranging from speculative positioning in Brent futures to subnational fiscal dependencies—the model becomes completely unstable. It loses its ability to isolate the core transmission channels that matter for interest rate decisions.
The Real Transmission Channel is Bank Capital, Not the Pump
The loudest critics focus entirely on consumer price index (CPI) indexing. They argue that because the Bank of England's models under-predicted the second-round effects of energy costs on wages, the entire framework is broken.
They are looking at the wrong variable.
The true danger of an energy shock to an economy like the United Kingdom does not live in the immediate retail price of petrol. It lives in the financial system's balance sheets. Recent macroeconomic research demonstrates that high oil prices exert a severe, asymmetric drag on bank balance sheets, particularly for highly leveraged institutions (Gelain & Lorusso, 2022).
When energy costs spike structurally, firms cut production and capital utilization drop. This asset degradation triggers a financial accelerator mechanism: bank asset values drop, lending capacity contracts, and a standard supply shock morphs into a systemic credit crunch (Gelain & Lorusso, 2022).
This is where the contrarian truth emerges: the Bank of England's structural models are actually highly effective because they focus on macro-financial stability rather than trying to guess the exact trajectory of retail inflation. The Bank's core mandate during a supply crisis is ensuring that the financial sector can absorb the shock without triggering a liquidity collapse.
By utilizing simplified, conservative assumptions about energy shocks, the Prudential Regulation Authority (PRA) forces commercial banks to hold countercyclical capital buffers. If the model were adjusted to be "smarter" and more reactive to short-term market dynamics, it would inherently become pro-cyclical—loosening capital requirements when oil dips temporarily, only to leave the system exposed when the next geopolitical conflict erupts.
Dismantling the Premise of the "Fix the Model" Questions
When analyzing public discourse around monetary policy, the same flawed premises appear repeatedly. Let's dismantle them cleanly.
Why can't central banks use machine learning to predict oil shocks?
This question assumes that an oil shock is a data-rich problem waiting to be solved by computational power. It isn’t. Machine learning excel at pattern recognition in stable environments. An energy shock caused by a sudden maritime blockade or an unprecedented diplomatic realignment is, by definition, a non-linear event with zero historical data parity. Relying on predictive algorithms trained on past data guarantees that you will be blind-sided by the unique structure of the next crisis.
Shouldn't inflation targets be flexible when energy prices spike?
This is the most dangerous "common-sense" take in financial journalism. The argument goes that if a central bank knows inflation is being driven by global oil supply rather than domestic demand, it should look through the shock and avoid raising rates.
This view ignores the role of psychological trust in monetary transmission. When large negative supply shocks occur, the economy's trajectory depends heavily on whether the public trusts the central bank's inflation target (De Grauwe & Ji, 2024). If a central bank starts shifting its target or explicitly modifying its models to excuse high inflation, that trust collapses. Once trust is compromised, inflation expectations unanchor, turning a temporary energy spike into permanent wage-price inflation (De Grauwe & Ji, 2024).
The Hidden Cost of the Unconventional Approach
To be absolutely fair, maintaining these rigid, aggregate structural models comes with a distinct downside. It means central banks will almost always look slow on the uptake during the initial phases of an inflationary spike.
Because their frameworks prioritize long-term macro-financial stability over short-term predictive agility, the Bank of England will consistently under-react or over-react in the first six months of a structural energy shift. It is an unavoidable trade-off. You can have a model that is finely tuned to the immediate data noise but risks blowing up the banking system during a black swan event, or you can have a blunt, resilient model that keeps the lights on but makes for terrible headlines. I choose the latter.
Stop demanding that central bankers become commodities traders. Their models are intentionally built as blunt instruments because the real world is too chaotic for scalprods. The flaw isn't that their models are wrong; the flaw is thinking they were ever meant to be right.
References
De Grauwe, P., & Ji, Y. (2024). Trust and monetary policy. Journal of Forecasting, 43(5), 903-931. https://doi.org/10.1002/for.3065
Cited by: 13
Gelain, P., & Lorusso, M. (2022). The US banks’ balance sheet transmission channel of oil price shocks. Working paper (Federal Reserve Bank of Cleveland), (22-33R). https://doi.org/10.26509/frbc-wp-202233
Cited by: 3
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053-1069. https://doi.org/10.1257/aer.99.3.1053
Cited by: 122
