MétaCan
Menu
Back to cohort
Record W2140270477

Exploring the Use of a Nonparametrically Generated Instrumental Variable in the Estimation of a Linear Parametric Equation

2004· preprint· en· W2140270477 on OpenAlex

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

VenueRePEc: Research Papers in Economics · 2004
Typepreprint
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInstrumental variableSmoothingParametric statisticsNonparametric statisticsVariable (mathematics)Kernel (algebra)Applied mathematicsEstimationKernel smootherMathematicsParametric modelEconometricsComputer scienceMathematical optimizationStatisticsKernel methodArtificial intelligenceEngineeringMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

The use of a nonparametrically generated instrumental variable in estimating a single-equation linear parametric model is explored, using kernel and other smoothing functions. The method, termed IVOS (Instrumental Variables Obtained by Smoothing), is applied in the estimation of measurement error and endogenous regressor models. Asymptotic and small-sample properties are investigated by simulation, using artificial data sets. IVOS is easy to apply and the simulation results exhibit good statistical properties. It can be used in situations in which standard IV cannot because suitable instruments are not available.

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)0.004
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
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 score0.244
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.472
GPT teacher head0.444
Teacher spread0.028 · 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