Integrating Expenditure and Income Data: What to Do with the Statistical Discrepancy?
In: A New Architecture for the U.S. National Accounts
AbstractThis discussion paper led to a publication in (D.W. Jorgenson, J.S. Landefeld, W.D. Nordhaus, eds.) 'A New Architecture for the U.S. National Accounts', NBER Studies in Income and Wealth , vol. 66, 309-54, University of Chicago Press, 2006. The purpose of this paper is to build consistent, integrated datasets to investigate whether various disaggregated data can shed light on the possible sources of the statistical discrepancy. Our strategy is first to use disaggregated data to estimate consistent sets of input-output models that sum to either GDP or GDI and compare the two in order to see where the discrepancy resides. We find a few âproblemâ industries that appear to explain most of the statistical discrepancy. Second, we explore what combination of the expenditure data and the income data seem to produce the most sensible data according to a few economic criteria. A mixture of data that do not aggregate either to GDP or to GDI appears optimal.
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Other versions of this item:
- J. Joseph Beaulieu & Eric J. Bartelsman, 2004. "Integrating expenditure and income data: what to do with the statistical discrepancy?," Finance and Economics Discussion Series 2004-39, Board of Governors of the Federal Reserve System (U.S.).
- J. Joseph Beaulieu & Eric J. Bartelsman, 2004. "Integrating Expenditure and Income Data: What to do with the Statistical Discrepancy?," Tinbergen Institute Discussion Papers 04-078/3, Tinbergen Institute.
- C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data
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