Reconciled Estimates of Monthly GDP in the US
Stuart McIntyre () and
Aubrey Poon ()
Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE)
In the US, income and expenditure side estimates of GDP (GDPI and GDPE) measure "true" GDP with error and are available at the quarterly frequency. Methods exist for producing reconciled quarterly estimates of GDP based on GDPI and GDPE. In this paper, we extend these methods to provide reconciled historical GDP estimates at the monthly frequency from 1960. We do this using a Bayesian Mixed Frequency Vector Autoregression involving GDPE, GDPI, unobserved true GDP and monthly indicators of short-term economic activity. We illustrate how the new monthly data contribute to our historical understanding of business cycles.
Keywords: Income; Output; Expenditure; Monthly; Business cycle; Expansion; Contraction; Recession; Turning point; State-space model; Vector autoregressions; Bayesian methods (search for similar items in EconPapers)
JEL-codes: E32 (search for similar items in EconPapers)
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Working Paper: Reconciled Estimates of Monthly GDP in the US (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:nsr:escoed:escoe-dp-2020-16
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