Interesting work on obtaining blended estimates of GDP:
Improving GDP Measurement: A Measurement-Error Perspective, by Boragan Aruoba, Francis X. Diebold, Jeremy Nalewaik, Frank Schorfheide, and Dongho Song, First Draft, January 2013 This Draft, May 2, 2013: Abstract: We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties -- particularly as regards serial correlation -- differ markedly from those of the standard expenditure-side measure and lead to substantially-revised views regarding the properties of GDP.
1 Introduction Aggregate real output is surely the most fundamental and important concept in macroeconomic theory. Surprisingly, however, significant uncertainty still surrounds its measurement. In the U.S., in particular, two often-divergent GDP estimates exist, a widely-used expenditure-side version, GDPE, and a much less widely-used income-side version, GDPI.1 Nalewaik (2010) and Fixler and Nalewaik (2009) make clear that, at the very least, GDPI deserves serious attention and may even have properties in certain respects superior to those of GDPE. That is, if forced to choose between GDP E and GDPI, a surprisingly strong case exists for GDPI. But of course one is not forced to choose between GDPE and GDPI, and a GDP estimate based on both GDPE and GDPI may be superior to either one alone. In this paper we propose and implement a framework for obtaining such a blended estimate. ...
The main result on the serial correlation properties is that the blended measure of "GDPM is highly serially correlated across all specifications (ρ≈.6), much more so than the current 'consensus' based on GDPE (ρ≈.3)."