Why Use This Data Source In Your Models?
Personal consumption expenditures measures consumer spending on goods and services. Data scientists use it as a measure for economic growth.
PCE: Excl Food & Energy - SA
U.S. Bureau of Economic Analysis
Personal Income and Outlays
Index 2012 = 100, Seasonally Adjusted
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.
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
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 0.00
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.57 p-value = 0.01 indicates that the data is not stationary.
The Shapiro-Wilk test returned W = 0.95 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 0.16 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.02 with a p-value of 1.00 inidcates the data is unimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
U.S. Bureau of Economic Analysis, Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) [PCEPILFE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCEPILFE, December 16, 2019.
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