READY SIGNAL CONTROL DATA

Personal Saving Rate

Why Use This Data Source In Your Models?

Personal saving rate is calculated as the ratio of personal saving to disposable income. Data scientist will use this rate as a proxy for future planning compared to immediate needs.

Personal Saving Rate

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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 seasonality. The data should be adjusted. While the Order Norm transformation, provides the best normality, the Untransformed 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.

Source:
U.S. Bureau of Economic Analysis

Release:
Personal Income and Outlays

Units:
Percent, Seasonally Adjusted Annual Rate

Frequency:
Monthly

Available Through:
07/31/2022

Suggested Treatment:

The data shows seasonality. The data should be adjusted. While the Order Norm transformation, provides the best normality, the Untransformed 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 does not show strong auto correlation indicating no need for differencing

The ACF indicates 0 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.18

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.18 p-value = 0.02 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 1.71 indicates the data are substantially skewed.

Hartigan's dip test score of 0.04 with a p-value of 0.26 inidcates the data is unimodal

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

No transform
1.41
Box-cox
1.59
Log_b(x-a)
1.52
sqrt(x+a)
1.46
exp(x)
3.56
arcsinh(x)
1.52
Yeo-Johnson
1.60
OrderNorm
1.37

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 Saving Rate [PSAVERT], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PSAVERT, December 16, 2019.

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