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Record W4408961219 · doi:10.1007/s00181-025-02727-y

Estimating flexible functional forms using macroeconomic data

2025· article· en· W4408961219 on OpenAlex
W. Erwin Diewert, Koji Nomura, Chihiro Shimizu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEmpirical Economics · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
FundersJapan Society for the Promotion of Science
KeywordsEconometricsEconomicsComputer scienceMathematical economics

Abstract

fetched live from OpenAlex

Abstract The paper estimates a flexible functional form for a joint cost function using US aggregate data for the years 1970–2022. There are four outputs (consumption, government, investment and exports) and six inputs (imports, labour, machinery and equipment services, structure services, other capital services and land services). Curvature conditions on the joint cost function are imposed without destroying the flexibility of the functional form. Various elasticities of supply and demand are estimated along with technical progress bias terms for each input. When using aggregate time series data based on the System of National Accounts, the paper shows that it is probably better to estimate a joint cost function rather than a gross output function or a GDP function. The paper also shows that assuming that an aggregate production function has constant elasticities of substitution is not appropriate for the US. Finally, the importance of including land as an aggregate input is stressed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.303
GPT teacher head0.339
Teacher spread0.036 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it