Bibliographic record
Abstract
Citation (2013), "List of Contributors", Structural Econometric Models (Advances in Econometrics, Vol. 31), Emerald Group Publishing Limited, Bingley, pp. vii-Viii. https://doi.org/10.1108/S0731-9053(2013)0000032017 Publisher: Emerald Group Publishing Limited Copyright © 2013 Emerald Group Publishing Limited Victor Aguirregabiria Department of Economics, University of Toronto, Toronto, ON, Canada Peter Arcidiacono Department of Economics, Duke University, Durham, NC, USA Patrick Bayer Department of Economics, Duke University, Durham, NC, USA Federico A. Bugni Department of Economics, Duke University, Durham, NC, USA Jiawei Chen Department of Economics, University of California-Irvine, Irvine, CA, USA Eugene Choo Department of Economics, University of Calgary, Calgary, Canada Federico Echenique Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA Alfred Galichon Department of Economics, Sciences Po, Paris, France Bryan S. Graham Department of Economics, University of California-Berkeley, Berkeley, CA, USA Yingyao Hu Department of Economics, Johns Hopkins University, Baltimore, MD, USA Jonathan James California Polytechnic State University, San Luis Obispo, CA, USA Ivana Komunjer Department of Economics, University of California-San Diego, La Jolla, CA, USA SangMok Lee Department of Economics, University of Pennsylvania, Philadelphia, PA, USA Arvind Magesan Department of Economics, University of Calgary, Calgary, Canada Matthew Shum Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA Shannon Seitz Analysis Group, Inc., Boston, MA, USA Wei Tan Hanqing Institute of Economics and Finance, Renmin University of China, Beijing, China Migiwa Tanaka Department of Economics, University of Toronto, Toronto, ON, Canada Kosuke Uetake School of Management, Yale University, New Haven, CT, USA Yasutora Watanabe Department of Management and Strategy, Kellogg School of Management, Northwestern University, Evanston, IL, USA Book Chapters Structural Econometric Models Advances in Econometrics Structural Econometric Models Copyright Page List of Contributors Introduction Euler Equations for the Estimation of Dynamic Discrete Choice Structural Models Approximating High-dimensional Dynamic Models: Sieve Value Function Iteration Identifying Dynamic Games with Serially Correlated Unobservables Partial Identification in Two-sided Matching Models Identification of Matching Complementarities: A Geometric Viewpoint Comparative Static and Computational Methods for an Empirical One-to-one Transferable Utility Matching Model A Test for Monotone Comparative Statics Estimating Supermodular Games Using Rationalizable Strategies Estimation of the Loan Spread Equation with Endogenous Bank-Firm Matching The Collective Marriage Matching Model: Identification, Estimation, and Testing Deflation in Durable Goods Markets: An Empirical Model of the Tokyo Condominium Market A Dynamic Analysis of the U.S. Cigarette Market and Antismoking Policies
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.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.726 | 0.743 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".