READY SIGNAL CONTROL DATA

Total Public Debt

Why Use This Data Source In Your Models?

Tracking Total Public Debt is essential for economists, policymakers, and financial analysts to understand a nation's fiscal health and economic stability. This metric represents the cumulative amount a government owes, encompassing both domestic and foreign obligations. For data scientists, it provides crucial historical data points for analyzing trends, debt burden, and its impact on the economy. For businesses, this metric offers insight into government fiscal policies, influencing interest rates and overall market stability. A rising public debt might lead to higher interest rates, impacting borrowing costs for companies. Financial analysts keenly track this data, evaluating its implications on investment portfolios and market dynamics. Understanding the national debt allows businesses to anticipate economic shifts, adjust financial strategies, and make informed investment choices. Additionally, economists analyze public debt to comprehend its impact on interest rates, inflation, and overall economic stability, allowing for informed monetary policy decisions. Total Public Debt serves as a barometer of a nation's fiscal responsibility, guiding financial planning and policy-making to safeguard economic prosperity.

Total Public Debt

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Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

Suggested Treatment:

The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Boxcox transformation, provides the best normality, the Yeo Johnson variable will also perform well.

Grain Transformation:

Data is able to be distributed by geography but not by time. The roll up method used is Sum.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 2 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.03

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.25 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 0.80 indicates the data are moderately skewed.

Hartigan's dip test score of 0.05 with a p-value of 0.69 inidcates the data is unimodal

Statistics (Pearson P/ df, lower => more normal)

No transform

3.00

Box-cox

1.22

Log_b(x-a)

1.41

sqrt(x+a)

1.33

exp(x)

arcsinh(x)

1.41

Yeo-Johnson

1.31

OrderNorm

1.62

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

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