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Reconciled Estimates of Monthly GDP in the US

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Abstract
In the US, income and expenditure-side estimates of GDP (GDPI and GDPE) measure "true" GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDPE, GDPI, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (that is, the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.

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  • Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Reconciled Estimates of Monthly GDP in the US," Working Papers 22-01, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwq:93615
    DOI: 10.26509/frbc-wp-202201
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    3. Bing Han & Muhammad Rizwanullah & Yane Luo & Rahim Atif, 2024. "The role of cross-border E-commerce on the export of goods and services," Electronic Commerce Research, Springer, vol. 24(2), pages 1367-1384, June.
    4. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Blagov, Boris & Krause, Clara & Schmidt, Torsten & Exß, Franziska & Heinisch, Katja & Holtemöller, Oliver, 2024. "Frühzeitige Ermittlung stabiler Ergebnisse zum Bruttoinlandsprodukt bzw. realen Wirtschaftswachstum und der Bruttowertschöpfung auf Länderebene. Endbericht," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 296879.
    6. Wu, Ping, 2024. "Should I open to forecast? Implications from a multi-country unobserved components model with sparse factor stochastic volatility," International Journal of Forecasting, Elsevier, vol. 40(3), pages 903-917.

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    More about this item

    Keywords

    Mixed frequency; Vector autoregressions; Bayesian methods; Nowcasting; Business;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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