Apples and Oranges in Macroeconomics: A Quiet Error with Loud Consequences

My uncle, once a mathematician and now an economist, recently made an intriguing—and slightly opaque—comment about macroeconomic policy. He said that it is often vitiated by a kind of conceptual confusion: the belief that instantaneous variables (like prices) can be straightforwardly read off of non-instantaneous variables (like GDP growth over a year). This, he said, leads to subtle but foundational misjudgments in policymaking—especially by central banks and treasury departments.

It’s a technical-sounding point, but I couldn’t shake the feeling that it mattered. So I asked him about it, and here’s what I pieced together—with some help from a deeper dive into economics, physics, and epistemology.

🧩 Instantaneous vs. Non-Instantaneous Variables

Let’s start with the basic distinction.

• Instantaneous variables: Prices, interest rates, inflation at a given moment. Think: “What’s the price of oil right now?”

• Non-instantaneous variables: Aggregated over time. GDP over a year, unemployment rate over a quarter, inflation over 12 months.

These are fundamentally different kinds of data: one is a snapshot, the other is a summary over time. Mathematically, they are the difference between a point and an integral, or a derivative and an average.

But in macroeconomics, these two types of variables are often blurred together.

🏛️ The Category Error

When central banks make decisions based on recent GDP growth or year-over-year inflation, they are often using lagging, time-smeared aggregates to infer or control instantaneous conditions in the economy. For example:

“GDP growth was 2.5% last year and inflation is still high → the economy is overheating → raise interest rates.”

But that GDP number includes economic activity from 12 months ago. It says very little about what’s happening now, or how prices are behaving today. The price level, an instantaneous variable, doesn’t cleanly derive from the GDP growth rate, a long-term aggregate.

This is what my uncle was pointing to: a deep confusion of levels. And when that confusion informs policy, it can lead to miscalibrated interventions, like raising rates just as inflation is softening.

📡 A Parallel in Physics

This isn’t just an economic issue—it has a counterpart in physics.

• In kinematics, you can’t infer instantaneous velocity from average speed.

• In thermodynamics, trying to define local temperature based on system-wide averages is often meaningless.

• In quantum mechanics, the expectation value (a statistical average) doesn’t tell you what a particle is doing at a given instant.

• In electrodynamics, the electric field at a point now depends not on the present state of a charge, but on its past motion (retarded potentials).

So physics, which demands tight conceptual hygiene, has long recognized that you can’t deduce a moment from a mean. Economics, by contrast, often still tries.

🤔 So Where Does This Distinction Fit in Economics?

Here’s the interesting part. While it’s a valid and important distinction, most economic predictions—e.g., “There will be a recession next year” or “Tariffs will cripple the economy”—are judged right or wrong based on empirical facts, not on whether the forecaster properly categorized variables.

So you might ask: is this just an ivory-tower concern?

Actually, no.

This distinction doesn’t usually affect the forecast, but it affects the models and the policy frameworks that generate the forecasts and responses. Here’s where it fits:

Layer	Function of the Variable-Type Distinction
Forecasting	Minor — often overshadowed by empirical judgment
Model Construction	Crucial — misclassifying variables undermines model validity
Post-Hoc Diagnostics	Important — helps explain why models or policies misfired
Epistemology	Foundational — pertains to what kind of knowledge economics can claim
Policy Design	Critical — prevents flawed interventions based on mismatched timescales

In other words, it’s not front-page stuff, but it’s load-bearing architecture. If you build policy or theory without it, you may not notice—until something cracks.

🧠 Where My Uncle Got It

This distinction isn’t standard-issue macroeconomics. It’s more likely something my uncle brought with him from his mathematical training, where the difference between a point and an integral is second nature, and category errors are immediately spotted.

Macroeconomics tends to prioritize models that use aggregates—GDP, CPI, etc.—and often doesn’t rigorously distinguish between temporal categories. So unless you have a formal background in math or physics, you might never even see the issue.

But once you do, it changes how you look at everything—from the shape of economic models to the logic of policy responses.

📌 Conclusion

The confusion between instantaneous and non-instantaneous variables may seem like an academic footnote, but it quietly underlies many of the errors and incoherencies in modern macroeconomic modeling and policy.

If economists and policymakers were more careful about these distinctions—if they treated inflation, price, and output as variables with different temporal and conceptual structures—we might get better models, better diagnostics, and less erratic policy.

As my uncle, with quiet exasperation, put it:

“They’re trying to read the clock by averaging over the calendar.”