CPI (Urban): All Items
Why Use This Data Source In Your Models?
The consumer price index is an index of the variation in prices paid by typical consumers for all items. This describes the overall economic situation, and supply and demand of all goods in the US.
CPI (Urban): All Items
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
U.S. Bureau of Labor Statistics
Release:
Consumer Price Index
Units:
Index 1982-1984=100, Seasonally Adjusted
Frequency:
Monthly
Available Through:
10/31/2024
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 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 2 order differencing is appropriate.
Further differencing is reccommended
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.46 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.89 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 1.11 indicates the data are substantially skewed.
Hartigan's dip test score of 0.03 with a p-value of 0.69 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. Bureau of Labor Statistics, Consumer Price Index for All Urban Consumers: All Items in U.S. City Average [CPIAUCSL], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CPIAUCSL, December 13, 2019.
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