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Backcasting National Accounts Data

Backcasting National Accounts Data. Examples from United States Experience. Brent Moulton. Advisory Expert Group on National Accounts Washington DC 9 September 2014. Why backcast economic data?. Provide a service to data customers Maintain time-series consistency

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Backcasting National Accounts Data

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  1. Backcasting National Accounts Data Examples from United States Experience Brent Moulton Advisory Expert Group on National Accounts Washington DC 9 September 2014

  2. Why backcast economic data? • Provide a service to data customers • Maintain time-series consistency • Produce longer time series to study changes in the economy over time • Understand sources of economic growth and productivity over time

  3. When is backcasting used? • Changes in classification • Industry and other classification systems • Changes in concepts • Newly recognized asset or redefined activity • Expanded detail • Sub-aggregate breakouts • When data are not available to directly measure the economic variables

  4. Approaches • Microdata approaches • Detailed reclassification of micro units • Macrodata approaches • Concordance tables • Proportional splicing • Interpolation/Backward extrapolation with or without indicator

  5. Examples in the US national accounts • GDP-by-industry estimates 1947 - 1997 • North American Industry Classification System (NAICS) • Reclassifications of exports and imports • For example, new treatment of merchandising in BPM6 • Recognition of R&D as fixed assets • Newly constructed measures of R&D investment

  6. GDP by industry and NAICS • U.S. statistical agencies implemented new classification system in different years • Economic Census data - 1997 • Tax data - 1998 • Employment and earnings data - 2001 • Prices - 2004 • Prior to 1998, GDP by industry was based on Standard Industrial Classification (SIC) • Users urged BEA to provide NAICS time series • Not feasible to convert source data to NAICS

  7. Backcasting GDP by industry • Designed a backcasting technique • 1997 concordance of detailed SIC to NAIC data • Backward extrapolate concordance with SIC source data • Create published level SIC – NAICS conversion matrices 1987-1997 • Convert published SIC estimates to NAICS • Conversion matrices for 1977-1986 had less SIC detail • For 1947-1976, 1977 matrix held constant • Vki, t-p = Vki, t-p · (nki, t-p / nki, t-p+1 ) Where: i = industry t = 1997 p = 1,…,10 k = VA component (output, intermediate inputs, compensation, GOS) n = conversion coefficient V = dollar value of VA component

  8. Evaluating results • Reasonableness and consistency checks • Growth rates compared to published SIC industries • Aggregation of industry level real value added compared against expenditure-based real GDP

  9. Recognition of R&D as fixed asset • 2013 NIPA comprehensive revision • New estimates of R&D output and investment • Less available and reliable data further back in time *Prior to 1981 - aggregate estimates deemed more reliable than detailed industry data – proportionally scaled detail to hit aggregates

  10. Summary • Many different reasons to backcast • Each instance has unique requirements • Necessitates resourcefulness and inventiveness • Need to weigh the benefit of backcasting against the resources required and the resulting quality of the estimates • Need a strong evaluation process

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