Air Revenue Ton Miles of Freight and Mail
Why Use This Data Source In Your Models?
Air Revenue Ton Miles (RTM) of Freight and Mail is a critical measure of air cargo activity, tracking the weight of goods and mail multiplied by the distance they are transported. This metric is essential for understanding trends in global trade, e-commerce, and high-value goods transportation. Fluctuations in RTM reflect shifts in demand for expedited shipping, changes in global supply chain dynamics, and economic conditions impacting trade volumes. For analysts, Air RTM provides valuable insights into the health of the logistics sector, the efficiency of air freight operations, and broader economic indicators tied to international trade and commerce.
Air Revenue Ton Miles of Freight and Mail
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Suggested Treatment:
Grain Transformation:
Source:
U.S. Bureau of Transportation Statistics
Release:
U.S. Bureau of Transportation Statistics
Units:
Ton Miles, Seasonally Adjusted
Frequency:
Monthly
Available Through:
01/31/2025
Suggested Treatment:
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Boxcox variable will also perform well.
Grain Transformation:
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Auto Correlation Analysis:
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.39
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.22 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.92 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 0.27 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.05 with a p-value of 0.01 inidcates the data is multimodal
Statistics (Pearson P/ df, lower => more normal)
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal Impact
Seasonal and Trend Decompostion
Citation:
U.S. Bureau of Transportation Statistics, Air Revenue Ton Miles of Freight and Mail [AIRRTMFMD11], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/AIRRTMFMD11, November 25, 2024.
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