Reconciled estimates of monthly GDP in the US

Gary Koop, Stuart McIntyre, James Mitchell, Aubrey Poon

Research output: Contribution to journalArticlepeer-review

Abstract

In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP (Formula presented.) and GDP (Formula presented.)) 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 GDP (Formula presented.), GDP (Formula presented.), unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (i.e., 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.

Original languageEnglish
Number of pages15
JournalJournal of Business and Economic Statistics
Early online date21 Mar 2022
DOIs
Publication statusE-pub ahead of print - 21 Mar 2022

Keywords

  • mixed frequency
  • vector autoregressions
  • Bayesian methods
  • nowcasting
  • business cycles
  • national accounts

Fingerprint

Dive into the research topics of 'Reconciled estimates of monthly GDP in the US'. Together they form a unique fingerprint.

Cite this