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GDP helps predict US air travel at 2 of 8 lags tested, while air travel helps predict GDP at no lag

US
US
GDP helps predict US air travel at 2 of 8 lags tested, while air travel helps predict GDP at no lag
Pairwise Granger p-values on the pre-COVID quarters 1990 Q1 to 2019 Q4; the strict Toda-Yamamoto test at its selected 5 lags gives p=0.009 for GDP helping to predict air travel and p=0.916 for the reverse
airtraffickonomics.com

In the United States, past income helps predict air travel at some lags and past air travel never helps predict income; a stricter version of the test agrees. Both are tests of prediction only.

What this shows: The chart plots Aviation → GDP and GDP → Aviation on a vertical axis of P-value (below 0.05 = significant).

Conditions and method

Status: confirmed (estimated in a small bivariate system; omitted macro drivers could alter the result).

Each bar tests whether the past of one series helps predict the other at that lag, on the US quarters before the pandemic: below 0.05, it does. A stricter version of the test, valid on trending series, has GDP significant at all 5 lag settings tried and air travel at none. These tests show prediction, not cause.

The cross-market panel chart tests the same US quarters with a different test at a single lag, so its reading of the United States can differ from this chart’s. That is a difference of test and lag choice, not a contradictory finding.

How these numbers are made

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