Backcasting National Accounts Data Examples from United States
Backcasting National Accounts Data Examples from United States Experience Brent Moulton Advisory Expert Group on National Accounts Washington DC 9 September 2014 www. bea. gov
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 www. bea. gov 2
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 www. bea. gov 3
Approaches ▪ Microdata approaches § Detailed reclassification of micro units ▪ Macrodata approaches § Concordance tables § Proportional splicing § Interpolation/Backward extrapolation with or without indicator www. bea. gov 4
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 BPM 6 ▪ Recognition of R&D as fixed assets § Newly constructed measures of R&D investment www. bea. gov 5
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 www. bea. gov 6
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: www. bea. gov i = industry t = 1997 p = 1, …, 10 k = VA component (output, intermediate inputs, compensation, GOS) n = conversion coefficient V = dollar value of VA component 7
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 www. bea. gov 8
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 Time period Source data Comments 1981 - present R&D expenditure surveys Detailed costs by industry (business, and economic census data academic, government); relatively consistent across time 1957 -1980* R&D expenditure surveys Less consistency of surveys across time 1953 -1956 Insufficient data Geometric interpolation 1920 -1953 Various research studies of R&D costs Selected years; straight line interpolation between data points *Prior to 1981 - aggregate estimates deemed more reliable than detailed industry data – proportionally scaled detail to hit aggregates www. bea. gov 9
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 www. bea. gov 10
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