A Balanced System of Industry Accounts for the U.S. and Structural Distribution of Statistical Discrepancy” BEA working paper
Bibliographic record
Abstract
(Do not quote without permission) This paper describes and illustrates a generalized least squares (GLS) reconciliation method that can efficiently incorporate all available information on initial data in reconciling a large system of disaggregated accounts and can accurately estimate industry distribution of statistical discrepancy. The GLS reconciliation method is applied to reconciling the 1997 GDP-by-industry accounts and the Input-output accounts. The former measure GDP by industry using industry gross income, and the latter measure GDP by industry as the residual between gross output and intermediate inputs. The GLS method produced balanced estimates and estimated the industry distribution of the statistical discrepancy. The results show that using reliability to reconcile different accounts produces statistically meaningful balanced estimates. The study demonstrates that reconciling a large system of disaggregated accounts is empirically feasible and computationally efficient. ________ *This paper represents the author’s views and does not necessarily represent official positions of the Bureau of Economic Analysis. I would like to thank Professors Dale Jorgenson, Williams Nordhaus and other participants for their helpful comments at the BEA Advisory Committee Meeting on May 19, 2006. I would also like to thank Tarek M. Harchaoui from Statistics Canada for his excellent discussion and helpful comments at the 2006 NBER_CIRW summer workshop on July 19. Thanks also go to the participants during the presentation at the 2006 joint meetings of the American Statistical Association. I would like to express my appreciation to my colleagues at BEA for their comments on the paper and to all staff members at BEA who provided data and other assistance in this project. I would like to express my appreciation to Zhi Wang who brought up the discussion of the subject and helped with computer programs at the beginning stage of the project. 1 I.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".