PCE: Excl Food & Energy - SA
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
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
U.S. Bureau of Economic Analysis
Release:
Personal Income and Outlays
Units:
Index 2012 = 100, Seasonally Adjusted
Frequency:
Monthly
Available Through:
02/29/2024
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 = 0.44 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
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 0.60 indicates the data are moderately skewed.
Hartigan's dip test score of 0.02 with a p-value of 0.99 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 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.