Why Use This Data Source In Your Models?
Total public debt as a percentage of GDP measures the amount of money that the US governemnt owes to outside entities against the total economic output of the US. This is indicative of GDP and public debt levels, as well as the voerall economic situation in the US.
Total Public Debt as Percent of GDP
U.S. Dept. of the Treasury, Fiscal Service
Percent of GDP, Seasonally Adjusted
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 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 -0.04
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.06 p-value = 0.10 indicates that the data is stationary.
The Shapiro-Wilk test returned W = 0.97 with a p-value =0.55 indicating the data follows a normal distribution.
A skewness score of -0.24 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.06 with a p-value of 0.37 inidcates the data is unimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
Federal Reserve Bank of St. Louis and U.S. Office of Management and Budget, Federal Debt: Total Public Debt as Percent of Gross Domestic Product [GFDEGDQ188S], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GFDEGDQ188S, December 19, 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.