Manufacturers' New Orders: Durable Goods
Why Use This Data Source In Your Models?
Manufacturers' new orders for durable goods reflects new orders placed with manufacturers for delivery of expensive factory goods that last 3 years or more. This indicates economic health and manufacturer behavior in the US.
Manufacturers' New Orders: Durable Goods
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Suggested Treatment:
Grain Transformation:
Source:
U.S. Census Bureau
Release:
Manufacturers' New Orders
Units:
Millions of dollars, Seasonally Adjusted
Frequency:
Monthly
Available Through:
03/31/2025
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 able to be distributed by time and geography. The roll up method used is Sum.
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 -0.25
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.18 p-value = 0.02 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.97 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 0.21 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.04 with a p-value of 0.26 inidcates the data is unimodal
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. Census Bureau, Manufacturers' New Orders: Durable Goods [DGORDER], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DGORDER, December 15, 2019.
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