Bibliographic record
Abstract
Citation (2016), "List of Contributors", Essays in Honor of Aman Ullah (Advances in Econometrics, Vol. 36), Emerald Group Publishing Limited, Bingley, pp. ix-xii. https://doi.org/10.1108/S0731-905320160000036007 Publisher: Emerald Group Publishing Limited Copyright © 2016 Emerald Group Publishing Limited Aziza Aipenova Department of Mechanics and Mathematics, Kazakh National University, Almaty, Kazakhstan Yonghong An Department of Economics, Texas A&M University, College Station, TX, USA Badi H. Baltagi Department of Economics and Center for Policy Research, Syracuse University, New York, Syracuse, USA Yong Bao Department of Economics, Purdue University, West Lafayette, IN, USA Alexander Chudik Federal Reserve Bank of Dallas, Dallas, TX, USA Yanqin Fan Department of Economics, University of Washington, Seattle, WA, USA Jean-Jacques Forneron Department of Economics, Columbia University, New York, NY, USA Amos Golan Department of Economics and Info-Metrics Institute, American University, Washington, DC, USA; Santa Fe Institute, Santa Fe, NM, USA Emmanuel Guerre School of Economics and Finance, Queen Mary, University of London, London, UK Ai Han Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, People’s Republic of China Matthew Harding Sanford School of Public Policy and Department of Economics, Duke University, Durham, NC, USA Jerry Hausman Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA Daniel J. Henderson Department of Economics, University of Alabama, Tuscaloosa, AL, USA Yongmiao Hong Department of Economics and Department of Statistical Sciences, Cornell University, Ithaca, NY, USA Cheng Hsiao Department of Economics, University of Southern California, Los Angeles, CA, USA; WISE, Xiamen University, China Yulia Kotlyarova Department of Economics, Dalhousie University, Halifax, NS, Canada Dong Li School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX, USA Qi Li Department of Economics, Texas A&M University, College Station, TX, USA Long Liu Department of Economics, College of Business, University of Texas, San Antonio, San Antonio, TX, USA Robin L. Lumsdaine Kogod School of Business and Info-Metrics Institute, American University, Washington, DC, USA; National Bureau of Economic Research (NBER), Cambridge, MA, USA Esfandiar Maasoumi Department of Economics, Emory University, Atlanta, GA, USA Carlos Martins-Filho Department of Economics, University of Colorado at Boulder, Boulder, CO, USA Kamiar Mohaddes Faculty of Economics and Girton College, University of Cambridge, Cambridge, UK Kairat Mynbaev International School of Economics, Kazakh-British Technical University, Almaty, Kazakhstan Serena Ng Department of Economics, Columbia University, New York, NY, USA Christopher J. Palmer Haas School of Business, University of California, Berkeley, Berkeley, CA, USA Christopher F. Parmeter Department of Economics, University of Miami, Miami, FL, USA M. Hashem Pesaran Department of Economics & USC Dornsife INET, University of Southern California, Los Angeles, CA, USA Peter C. B. Phillips Department of Economics, Yale University, New Haven, CT, USA Jeffrey S. Racine Department of Economics and Graduate Program in Statistics, McMaster University, Hamilton, ON, Canada Mehdi Raissi International Monetary Fund, Washington, DC, USA Eric Renault Department of Economics, Brown University, Providence, RI, USA Marcia M. A. Schafgans Department of Economics, London School of Economics, London, UK Daniela Scidá Department of Economics, Brown University, Providence, RI, USA Liangjun Su School of Economics, Singapore Management University, Singapore, Singapore Shouyang Wang Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, People’s Republic of China Ximing Wu Department of Agricultural Economics, Texas A&M University, College Station, TX, USA Xin Yun Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, People’s Republic of China Yonghui Zhang School of Economics, Renmin University, Beijing, People’s Republic of China Yu Yvette Zhang Department of Agricultural Economics, Texas A&M University, College Station, TX, USA Yifeng Zhu Department of Economics, Emory University, Atlanta, GA, USA Victoria Zinde-Walsh Department of Economics, McGill University, Montréal, QC, Canada Book Chapters Essays in Honor of Aman Ullah Advances in Econometrics Essays in Honor of Aman Ullah Copyright Page List of Contributors Introduction Acknowledgments Photos Part I: Tribute A Selective Review of Aman Ullah’s Contributions to Econometrics Part II: Panel Data Models Semiparametric Estimation of Partially Linear Varying Coefficient Panel Data Models Testing for Spatial Lag and Spatial Error Dependence in a Fixed Effects Panel Data Model Using Double Length Artificial Regressions Long-Run Effects in Large Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors Semiparametric Estimation of Partially Linear Dynamic Panel Data Models with Fixed Effects Part III: Finite Sample Econometrics Finite-Sample Bias of the Conditional Gaussian Maximum Likelihood Estimator in ARMA Models Finite Sample BIAS Corrected IV Estimation for Weak and Many Instruments Part IV: Information and Entropy On the Construction of Prior Information – An Info-Metrics Approach The Wage Premium of Naturalized Citizenship Causality and Markovianity: Information Theoretic Measures Part V: Issues in Econometric Theory A Likelihood-Free Reverse Sampler of the Posterior Distribution A Vector Autoregressive Moving Average Model for Interval-Valued Time Series Data Inference in Near-Singular Regression Part VI: Nonparametric and Semiparametric Methods Multivariate Local Polynomial Estimators: Uniform Boundary Properties and Asymptotic Linear Representation Model Averaging Over Nonparametric Estimators Smoothness: Bias and Efficiency of Nonparametric Kernel Estimators A Class of Nonparametric Density Derivative Estimators Based on Global Lipschitz Conditions Local Polynomial Derivative Estimation: Analytic or Taylor? A Simple Consistent Nonparametric Estimator of the Lorenz Curve
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 | 0.007 | 0.008 |
| 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) | 0.729 | 0.762 |
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".