Why Use This Data Source In Your Models?
Real personal consumption expenditures measures the capital spent on goods and services in the US each month. This is indicative of wage rates, disposable income, and financial status of the average citizen. This is also indicative of overall economic health.
All Personal Comsumption Expenditures
U.S. Bureau of Economic Analysis
Personal Income and Outlays
Billions of Dollars, Seasonally Adjusted Annual Rate
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 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.
Further differencing is reccommended
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.53 p-value = 0.01 indicates that the data is not stationary.
The Shapiro-Wilk test returned W = 0.94 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 0.24 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.02 with a p-value of 0.98 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.
Use our platform to aggregate, normalize, and profile open source and premium control data. Spend less time finding and wrangling data, and more time building efficient and feature-rich machine learning data pipelines.
Instantly apply industry-standard
data science treatments and transformations, including (but not limited to) Differencing, Lead/Lag, Box Cox. Easily manipulate data across different time and geographic grains.
Our Patent Pending iterative testing engine allows you to upload your target variable, and the platform will test for possible statistical relationships across all available data sources. Saving you time and removing analyst bias.
Easily integrate your Ready Signal data to the data science platform of your choice. Connect directly to Ready Signal through our API or using one of our pre-built data connectors or download directly in Excel or CSV format.