Real GDP, Total US, Seas Adj Annual Rate
Why Use This Data Source In Your Models?
Real Gross Domestic Product (GDP) for the United States, seasonally adjusted at an annual rate, serves as a crucial economic indicator for analysts, policymakers, and businesses. The seasonally adjusted metric offers a refined view of economic performance by removing the influence of seasonal variations, providing a clearer picture of underlying trends. GDP is indicative of recessions, wages, & the employment situation. For data scientists, it acts as a reliable dataset for analyzing long-term economic patterns and making accurate forecasts, unencumbered by the distortions caused by seasonal fluctuations. Policymakers rely on this adjusted data to craft responsive economic policies, ensuring stability and growth regardless of seasonal influences. Businesses utilize this metric to plan production, manage inventories, and align marketing strategies, aligning their operations with consistent economic trends. Investors trust seasonally adjusted real GDP figures to assess economic health, guiding investment decisions in diverse sectors. Economists leverage this data to identify structural changes, offering valuable insights into the economy's fundamental strengths and weaknesses. In essence, Real GDP, Total US (Seas Adj Annual Rate), provides a refined lens through which stakeholders can analyze the enduring economic health of the nation, enabling them to make informed decisions in a dynamic and ever-changing economic landscape.
Real GDP, Total US, Seas Adj Annual Rate
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Suggested Treatment:
Grain Transformation:
Source:
U.S. Bureau of Economic Analysis
Release:
Gross Domestic Product
Units:
Billions of Chained 2012 Dollars, Seasonally Adjusted Annual Rate
Frequency:
Quarterly
Available Through:
12/31/2023
Suggested Treatment:
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Untransformed transformation, provides the best normality, the Arcsin 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 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 -0.28
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.08 p-value = 0.10 indicates that the data is stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.95 with a p-value =0.07 indicating the data follows a normal distribution.
A skewness score of -0.01 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.04 with a p-value of 0.88 inidcates the data is unimodal
Statistics (Pearson P/ df, lower => more normal)
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 [GDPC1], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GDPC1, December 19, 2019.
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