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Is GDP or GDI a better measure of output? A statistical approach

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  • Ryan Greenaway-McGrevy

    (Bureau of Economic Analysis)

Abstract

Gross domestic product (GDP) and gross domestic income (GDI) are in theory estimates of the same concept, namely economic production over a defined span of time and space. Yet the two measures are compiled using different source data, and the two measures often give different indications of the direction of the economy. This raises the issue of which of the two measures is a more accurate estimate of economic production. In this paper we present a time-series statistical framework for addressing this issue. Our findings indicate that the latest vintage of GDP has been a better measure of true output over the 1983-2009 period than the latest vintage of GDI. Our model also implies an optimal weighting of GDP and GDI can yield a more accurate estimate of economic output than either GDP or GDI alone. Our empirical findings indicate that a weighting of approximately 60% to GDP yields the best estimate for the 1983-2009 period. When we consider vintages of estimated output, we find that GDI often contains additional information to GDP regarding true output.

Suggested Citation

  • Ryan Greenaway-McGrevy, 2011. "Is GDP or GDI a better measure of output? A statistical approach," BEA Working Papers 0076, Bureau of Economic Analysis.
    • Handle: RePEc:bea:wpaper:0076
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    File URL: https://www.bea.gov/system/files/papers/WP2011-8.pdf
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    References listed on IDEAS

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    Cited by:

    1. Martín Almuzara & Gabriele Fiorentini & Enrique Sentana, 2023. "Aggregate Output Measurements: A Common Trend Approach," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 3-33, Emerald Group Publishing Limited.
    2. Mary C. Daly & John G. Fernald & Òscar Jordà & Fernanda Nechio, 2013. "Shocks and Adjustments," Working Paper Series 2013-32, Federal Reserve Bank of San Francisco.
    3. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    4. Tincho Almuzara & Dante Amengual & Enrique Sentana, 2017. "Normality Tests for Latent Variables," Working Papers wp2018_1708, CEMFI.
    5. Martín Almuzara & Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2024. "GDP Solera: The Ideal Vintage Mix," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 984-997, July.
    6. Martín Almuzara & Dante Amengual & Enrique Sentana, 2019. "Normality tests for latent variables," Quantitative Economics, Econometric Society, vol. 10(3), pages 981-1017, July.

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    More about this item

    JEL classification:

    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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