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Acknowledgments

2016· other· en· W4243520502 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingHonourLibrary scienceHonorSpatial econometricsManagementSociologyPolitical scienceStatisticsEconomicsComputer scienceMathematicsLaw

Abstract

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Citation (2016), "Acknowledgments", Essays in Honor of Aman Ullah (Advances in Econometrics, Vol. 36), Emerald Group Publishing Limited, Bingley, pp. xxiii-xxiv. https://doi.org/10.1108/S0731-905320160000036009 Publisher: Emerald Group Publishing Limited Copyright © 2016 Emerald Group Publishing Limited A conference in honour of Aman Ullah took place at Riverside Mission Inn on March 13–15, 2015 to celebrate his long and productive academic career. We would like to thank the following sponsors for their support (in alphabetical order): editors of Advances in Econometrics, Emerald Group Publishing Ltd., Info-Metrics Institute of American University, International Association for Applied Econometrics (IAAE) and the University of California Riverside’s College of Humanities Arts and Social Sciences, Department of Economics, Department of Statistics, School of Business Administration and School of Public Policy. We also like to express our thanks to the following UC-Riverside staff: Damaris Carlos, Gary Kuzas, Mark Manalang, Tanya Wine (and Mark Wine) and Patricia Winkler, and two of our graduate students: Bai Huang and Najrin Khanom. Their hard work and dedication before, during, and after the event made our work much easier. This AIE volume is a collection of papers, most of them presented at the conference, on topics in which Aman Ullah has made major contributions – nonparametric and semiparametric econometrics, finite sample econometrics, shrinkage methods, information/entropy econometrics, model specification testing, robust inference and panel/spatial models. Each paper was rigorously refereed by two or three experts. We would like to thank the following reviewers for their critical and constructive reviews (in alphabetical order): Deniz Baglan (Howard University), Yong Bao (Purdue University), Martin Burda (University of Toronto), Zongwu Cai (University of Kansas), Matias Cattaneo (University of Michigan), John Chao (University of Maryland), Juan Carlos Escanciano (Indiana University), Yanqin Fan (University of Washington), Jiti Gao (Monash University), Amos Golan (American University), William Greene (New York University), Daniel Henderson (University of Alabama), Eric Hillebrand (University of Aarhus), Cheng Hsiao (University of Southern California), Emma Iglesias (University of Coruña), Atsushi Inoue (Vanderbilt University), David Jacho-Chavez (Emory University), Ivan Jeliazkov (University of California, Irvine), Fei Jin (Shanghai University of Finance and Economics), Degui Li (University of York), Dong Li (University of Texas Dallas), Qi Li (Texas A&M), Wei Lin (Capital University of Economics and Business), Chu-An Liu (Academia Sinica), Essie Maasoumi (Emory University), Ron Mittelhammer (Washington State University), Kusum Mundra (Rutgers University), Kelley Pace (Louisiana State University), Christopher Parmeter (University of Miami), Hashem Pesaran (University of Southern California), Jeff Racine (McMaster University), Chris Skeels (University of Melbourne), Xiaojun Song (Peking University), Liangjun Su (Singapore Management University), Yuying Sun (Chinese Academy of Sciences), Abderrahim Taamouti (Durham University), Yundong Tu (Peking University), Aman Ullah (University of California, Riverside), Alan Wan (City University of Hong Kong), Ying Wang (Peking University), Ximing Wu (Texas A&M University), Ke-Li Xu (Texas A&M University), Jui-Chung Ray Yang (University of Southern California), Zhenlin Yang (Singapore Management University) and Yonghui Zhang (Renmin University). We would also like to thank the following participants, who made presentations and/or chaired sessions: Deniz Baglan (Howard University), Anil Bera (University of Illinois), Tom Fomby (Southern Methodist University), Yan Ge (Central University of Finance and Economics), Xiao Huang (Kennesaw State University), Wei Lin (Capital University of Economics and Business), Ruixuan Liu (Emory University), Xiangdong Long (Bank of Communications Schroder Fund Management), Shujie Ma (University of California, Riverside), Kusum Mundra (Rutgers University), Xiaojun Song (Peking University), Yundong Tu (Peking University), Yun Wang (University of International Business and Economics) and Jie Wei (Huazhong University of Science and Technology). 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.340
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.066

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 head0.009
GPT teacher head0.262
Teacher spread0.254 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2016
Admission routes1
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