Why Use This Data Source In Your Models?
Total wages and salaries for each US state measures the sum of money paid to employees in that state over the past 3 months. This is indicative of hourly wage rates, economic health, cost of living, and rural vs urban demographics.
Total Wages and Salaries by State
U.S. Bureau of Labor Statistics
Thousands of Dollars, Seasonally Adjusted Annual Rate
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Yeo Johnson transformation, provides the best normality, the Arcsin variable will also perform well.
Data is able to be distributed by time but not by geography. The roll up method used is Sum.
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
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.12 p-value = 0.10 indicates that the data is stationary.
The Shapiro-Wilk test returned W = 0.93 with a p-value =0.05 indicating the data follows a normal distribution.
A skewness score of -0.01 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.06 with a p-value of 0.63 inidcates the data is unimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
The following states do not report for this feature: District of Columbia, Puerto Rico.
Federal Reserve Bank of St. Louis and U.S. Bureau of Economic Analysis, Total Wages and Salaries, retrieved from FRED, Federal Reserve Bank of St. Louis; January 27, 2020.
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