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Record W2285585996 · doi:10.1002/jae.2505

Transitions at Different Moments in Time: A Spatial Probit Approach

2016· article· en· W2285585996 on OpenAlex
J. Paul Elhorst, Pim Heijnen, Anna Samarina, Jan Jacobs

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

VenueJournal of Applied Econometrics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsProbit modelEconometricsProbitVariable (mathematics)Inflation (cosmology)Sample (material)Duration (music)Ordered probitState (computer science)Binary numberMoment (physics)StatisticsState variableEconomicsSpatial dependenceMathematicsPhysics

Abstract

fetched live from OpenAlex

Summary This paper adopts a spatial probit approach to explain interaction effects among cross‐sectional units when the dependent variable takes the form of a binary response variable and transitions from state 0 to 1 occur at different moments in time. The model has two spatially lagged variables: one for units that are still in state 0 and one for units that had already transferred to state 1. The parameters are estimated on observations for those units that are still in state 0 at the start of the different time periods, whereas observations on units after they transferred to state 1 are discarded, just as in the literature on duration modeling. Furthermore, neighboring units that had not yet transferred may have a different impact from units that had already transferred. We illustrate our approach with an empirical study of the adoption of inflation targeting for a sample of 58 countries over the period 1985–2008. Copyright © 2016 John Wiley & Sons, Ltd.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
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
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.193
Teacher spread0.161 · 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