Editor’s note: Written by Aaron Terrazas, director of economic research at Convoy, a digital freight network. This is one in a series of periodic guest columns by industry thought leaders.
For most Americans, December is a month for celebration and reflection. For economists, it’s also a month for predictions.
Each year as we count down to the New Year, there are a lot of predictions about what to expect for the year ahead. There’s something in the December air that piques curiosity about the future for just about everyone in the freight world from chief executives down to frontline transportation managers.
Having been part of this annual drumbeat — in freight and in other industries — for the better part of the past decade, I’ve learned to read between the lines of these end-of-year predictions. Not all predictions are created equal, and not all are useful in guiding business decisions. Here are some tips that help me get the most out of the annual prediction season.
Ask for specifics
Everything’s a bullseye when you don’t specify the target: It’s almost always possible to carefully phrase predictions to increase the likelihood that they’ll turn out to be correct: Saying it’ll rain tomorrow in Seattle is perhaps less novel than saying it’ll rain at 2 p.m. tomorrow.
We often see this when end-of-year freight market predictions speak in broad generalities lacking in detail. For instance, predictions such as: “Rates will move lower [higher] next year,” or “Rates will be lower [higher] by this time next year.”
But statements like that don’t include details like how much lower, or when next year? The direction alone is not sufficient. It’s like Wall Street’s permabears: Sooner or later they’re bound to be right, but the timing and the magnitude are more actionable (but much harder to predict).
Demand detail about the range of potential outcomes
Models, both the formal statistical kind and in the informal rules-of-thumb we use in everyday life, help us to communicate our degree of certainty, or lack thereof, in our predictions.
For formal forecasts, confidence means including a statistical measure of uncertainty to show the range of probabilistic outcomes. But for less formal predictions conveying confidence is important too: It can be as simple as stating the forecaster’s subjective confidence in the outcome — e.g., on a scale from “very confident” to “not at all confident”.
Too often, it’s convenient to omit that detail — because we don’t want to go through the trouble of estimating (or admitting) it, or because it makes our models look weak when the confidence intervals are so laughably wide as to be useless for practical decision-making. Demand that level of granularity; otherwise, you’re getting an incomplete story.
Check what’s under the hood
All predictions are based on assumptions. Assumptions about the future and assumptions about how the world works. Assumptions that it’s better to rely exclusively on the data series you have, or assumptions to bring in other information. Assumptions that your data approximately mirrors the world as it is, or assumptions that it doesn’t.
For example, in freight, some forecasts rely only on time series movements without incorporating outside data, while others lean more heavily on outside data. Without knowing the underlying assumptions, it’s impossible to gauge how much to trust a particular prediction.
The intended end use of any prediction plays a critical role in how any forecaster will frame and present their expectations. Any consumer of these predictions should ask: What were the forecaster’s goals and how did those goals influence the analysis and conclusions? Some forecasts are intended to inform, others are intended to entertain — a distinction we should be particularly conscious of when it comes to the end of year prediction frenzy.
2020 wasn’t just a year that threw our predictions under the bus, it flipped the gears into reverse and rolled over them several times. In the face of years like the past, it’s easy to lose faith in our ability to predict the future. This is true when it comes to freight forecasts as much as it is true for forecasts about broader economy, the weather, or whether you’ll encounter traffic on the highway.
Despite their flaws and limitations, predictions are still essential to any business; in freight, predictions for truck rates and volumes help us set expectations and make plans. But for them to be useful, they need to be specific enough to be actionable and transparent enough to be understood.
Editor’s note: Aaron Terrazas is Director of Economic Research at Convoy, a digital freight network. He was previously an economist at Zillow and at the U.S. Treasury Department.
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