Manufacturing Employees - Seas Adj
Why Use This Data Source In Your Models?
Total manufacturing employees measures the number of persons 16 years of age or older working in manufacturing positions. This indicates population size and occupation opportunity.
Manufacturing Employees - Seas Adj
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
U.S. Bureau of Labor Statistics
Release:
Employment Situation
Units:
Thousands of Persons, Seasonally Adjusted
Frequency:
Monthly
Available Through:
08/31/2023
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 Yeo Johnson variable will also perform well.
Grain Transformation:
Data is able to be distributed by time but not by 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 -0.04
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.23 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.96 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of -0.11 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.05 with a p-value of 0.02 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 Labor Statistics, All Employees, Manufacturing [MANEMP], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MANEMP, December 16, 2019.
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