READY SIGNAL CONTROL DATA

Real GDP, Total US, Not Seas Adj

Why Use This Data Source In Your Models?

Real Gross Domestic Product (GDP) for the United States, not seasonally adjusted, stands as a foundational metric for economists, policymakers, and businesses, offering an unvarnished perspective on the nation's economic performance. Unlike its seasonally adjusted counterpart, this metric provides raw data, untainted by adjustments for seasonal fluctuations, offering a clear view of economic growth. For data scientists, it serves as a critical tool for analyzing short-term economic trends and identifying patterns that might be obscured by seasonal adjustments. Policymakers rely on this unadjusted data to gauge economic stability and formulate timely fiscal and monetary policies. Businesses use real GDP figures to assess market demand, aiding in production planning and inventory management. Investors leverage this raw data to gauge economic resilience, guiding investment decisions. Economists use this metric to conduct in-depth analyses of economic cycles and trends, providing essential information for long-term economic forecasts. In essence, Real GDP, Total US (Not Seas Adj), offers a transparent window into the core of the U.S. economy, empowering stakeholders to make informed decisions in a rapidly changing economic landscape.

Real GDP, Total US, Not Seas Adj

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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 seasonality. The data should be differenced and seasonally adjusted.

Grain Transformation:

Data is able to be distributed by time and geography. The roll up method used is Sum.

Source:
U.S. Bureau of Economic Analysis

Release:
Gross Domestic Product

Units:
Billions of Chained 2012 Dollars, Not Seasonally Adjusted

Frequency:
Quarterly

Available Through:
12/31/2023

Suggested Treatment:

The data shows auto correlation and seasonality. The data should be differenced and seasonally adjusted.

Grain Transformation:

Data is able to be distributed by time and geography. The roll up method used is Sum.

Auto Correlation Analysis:

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 -0.34

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.10 p-value = 0.10 indicates that the data is stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.97 with a p-value =0.26 indicating the data follows a normal distribution.

A skewness score of 0.00 indicates the data are fairly symmetrical.

Hartigan's dip test score of 0.03 with a p-value of 0.99 inidcates the data is unimodal

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

No transform
0.60
Box-cox
1.36
Log_b(x-a)
1.40
sqrt(x+a)
1.36
exp(x)
NA
arcsinh(x)
1.40
Yeo-Johnson
1.36
OrderNorm
1.53

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, Real Gross Domestic Product [ND000334Q], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/ND000334Q, December 19, 2019.

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