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Reconciled Estimates of Monthly GDP in the US

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.

Keywords: Mixed frequency; Vector autoregressions; Bayesian methods; Nowcasting; Business cycles; National accounts.
JEL codes: C32, E01, E32.

Suggested citation: Koop, Gary, Stuart McIntyre, James Mitchell, and Aubrey Poon. 2022. "Reconciled Estimates of Monthly GDP in the US. Working Paper No. 22-01. Federal Reserve Bank of Cleveland.

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