GDPplus (developed in Aruoba, Diebold, Nalewaik, Schorfheide and Song, "Improving GDP Measurement: A Measurement-Error Perspective," NBER Working Paper 18954, 2013) is based on a dynamic-factor model,
(GDPEtGDPIt)=(11)GDPt+(ϵEtϵIt)
GDPt=μ(1−ρ)+ρGDPt−1+ϵGt,
where GDPE and GDPI are noisy indicators of latent true GDP, ϵE and ϵI are expenditure- and income-side stochastic measurement errors, and ϵG is a stochastic shock to true GDP. The Kalman smoother provides an optimal estimate of GDP based on the noisy indicators GDPE and GDPI. That optimal estimate is GDPplus. Note that GDPplus is not just a period-by-period simple average, or even a weighted average, of GDPE and GDPI, because optimal signal extraction averages not only across the GDPE and GDPI series, but also over time.
The historical perspective on GDP provided by GDPplus complements the real-time perspective on the overall business cycle provided by the Aruoba-Diebold-Scotti (ADS) Index, also published by the Federal Reserve Bank of Philadelphia.
Moving forward, GDPplus will be updated at 2 PM on every day that new and/or revised GDPE and/or GDPI data are released. The next update will be November 7, the day of BEA's NIPA release for Q3 (delayed due to the government shutdown).
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