Panel Data Econometrics: Theory

Total Page:16

File Type:pdf, Size:1020Kb

Panel Data Econometrics: Theory Panel Data Econometrics This page intentionally left blank Panel Data Econometrics Theory Edited By Mike Tsionas Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-814367-4 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Candice Janco Acquisition Editor: Scott Bentley Editorial Project Manager: Susan Ikeda Production Project Manager: Maria Bernard Cover Designer: Miles Hitchen Typeset by SPi Global, India Dedication Dedicated to John F. Geweke. This page intentionally left blank Contents Contributors xv Foreword xvii General Introduction xix 1. A Synopsis of Econometrics 1 Stephen G. Hall 1 Introduction 1 2 Some Basic Concepts 2 3 Two Basic Approaches to Estimation 5 4 Maximum Likelihood (ML) 5 5 Generalized Method of Moments (GMM) 10 6 Some Examples of Moment Conditions 12 7 The Standard Linear Model and Least Squares 13 8 Failure of E(uu0) 5 σ2I 14 9 The Vector xt is Stochastic 14 lim ÀÁ0 10 Failure of x u ¼ 014 t ! ∞ T lim ÀÁ0 11 Failure of x x ¼ Ω 15 t ! ∞ T 12 Cointegration 16 13 Conclusion 17 Appendix Order of Magnitude and Convergence 18 References 19 2. Testing and Correcting for Endogeneity in Nonlinear Unobserved Effects Models 21 Wei Lin and Jeffrey M. Wooldridge 1 Introduction 21 2 Models Linear in Parameters 23 3 Exponential Model 28 3.1 An FE Poisson/CF Approach 28 3.2 Estimating Average Partial Effects 30 3.3 A CRE/Control Function Approach 31 4 Probit Response Function 32 5 Other Nonlinear Models 34 vii viii Contents 5.1 Pooled Methods 34 5.2 Joint Estimation Methods 35 6 Empirical Example 36 7 Extensions and Future Directions 38 Appendix 39 A.1 Relationship Between the FE and Mundlak Residuals 39 A.2 Equivalence in Using the FE and Mundlak Residuals in FE Poisson Estimation 40 References 42 Further Reading 43 3. Nonlinear and Related Panel Data Models 45 William Greene and Qiushi Zhang 1 Introduction 45 2 Nonlinear Models 48 2.1 Coefficients and Partial Effects 50 2.2 Interaction Effects 51 2.3 Identification Through Functional Form 51 2.4 Endogeneity 52 3 Panel Data Models 53 3.1 Objects of Estimation 54 3.2 General Frameworks 58 3.3 Dynamic Models 60 4 Nonlinear Panel Data Modeling 61 4.1 Fixed Effects 61 4.2 Random Effects Estimation and Correlated Random Effects 68 4.3 Robust Estimation and Inference 71 4.4 Attrition 73 4.5 Specification Tests 74 5 Panel Data 76 6 Modeling Frameworks and Applications 78 6.1 Binary Choice 78 6.2 Bivariate and Recursive Binary Choice 84 6.3 Ordered Choice 85 6.4 Censored or Truncated Regression 85 6.5 Stochastic Frontier: Panel Models 85 6.6 Count Data 86 6.7 A General Nonlinear Regression 88 6.8 Sample Selection Models 88 6.9 Individual Choice and Stated Choice Experiments 89 6.10 Multilevel Models Hierarchical (Nonlinear) Models 90 6.11 Fixed Effects With Large N and Large T 90 References 91 Contents ix 4. Nonparametric Estimation and Inference for Panel Data Models 97 Christopher F. Parmeter and Jeffrey S. Racine 1 Introduction 97 2 How Unobserved Heterogeneity Complicates Estimation 98 3 Estimation in the Random Effects Framework 99 3.1 Preliminaries 99 3.2 Local-Polynomial Weighted Least-Squares 100 3.3 Spline-Based Estimation 103 3.4 Profile Likelihood Estimation 106 4 Estimation in the Fixed Effects Framework 108 4.1 Differencing/Transformation Methods 108 4.2 Profile Estimation 110 4.3 Marginal Integration 112 4.4 Profile Least Squares 113 5 Dynamic Panel Estimation 116 5.1 The Static Setting 117 6 Inference 118 6.1 Poolability 118 6.2 Specification Testing 122 6.3 A Hausman Test 124 6.4 Simultaneous Confidence Bounds 125 7 Conclusions 127 Acknowledgments 127 References 127 5. Heterogeneity and Endogeneity in Panel Stochastic Frontier Models 131 Levent Kutlu and Kien C. Tran 1 Introduction 131 2 Panel Stochastic Frontier Models With Heterogeneity 132 3 Panel Stochastic Frontier Models With Endogeneity 138 4 Panel Stochastic Frontier Models With Both Heterogeneity and Endogeneity 141 5 Concluding Remarks 143 References 143 6. Bayesian Estimation of Panel Count Data Models: Dynamics, Latent Heterogeneity, Serial Error Correlation, and Nonparametric Structures 147 Stefanos Dimitrakopoulos 1 Introduction 147 2 Bayesian Preliminaries 150 2.1 Bayesian Statistics 150 x Contents 2.2 Markov Chain Monte Carlo Simulation Methods 150 2.3 Bayesian Nonparametric Models 152 3 Parametric Panel Count Data Regression Models 157 3.1 The Static Poisson Model 157 3.2 Extension I: The Dynamic Poisson Model 157 3.3 Extension II: The Dynamic Poisson Model With Latent Heterogeneity 158 3.4 Extension III: The Dynamic Poisson Model With Latent Heterogeneity and Serial Error Correlation 159 4 Semiparametric Panel Count Data Regression Models 160 5 Prior-Posterior Analysis 161 5.1 The Models of Interest 161 5.2 Prior Specification 162 5.3 Posterior Sampling 162 5.4 Model Comparison 170 6 Conclusions 171 References 171 Further Reading 173 7. Fixed Effects Likelihood Approach for Large Panels 175 Chihwa Kao and Fa Wang 1 Introduction 175 2 Notations and Preliminaries 177 3 Fixed Dimensional Case 179 3.1 Consistency 179 3.2 Asymptotic Expansion 180 3.3 Bias Calculation 181 4 Panel With Individual Effects 181 4.1 Consistency 181 4.2 Asymptotic Expansion 182 4.3 Bias Calculation 188 5 Panel With Individual Effects and Time Effects 188 5.1 Consistency 189 5.2 Asymptotic Expansion 189 5.3 Bias Calculation 193 6 Conclusions 195 Acknowledgment 195 References 196 8. Panel Vector Autoregressions With Binary Data 197 Bo E. Honore and Ekaterini Kyriazidou 1 Introduction 197 2 The Univariate Logit Model 199 2.1 Static Case 199 Contents xi 2.2 Dynamic Case (Pure AR(1)) 202 2.3 Dynamic Case With Exogenous Covariates 204 3 The Bivariate Pure VAR(1) Logit Case 206 4 The Bivariate Logit Model With Exogenous Covariates 209 5 The General M-Variate, General T VAR(1) Case 212 6 Contemporaneous Cross-Equation Dependence 213 6.1 Static Case 213 6.2 Dynamic Case 216 7 Monte Carlo Experiments 219 8 Conclusions 220 Acknowledgments 221 References 221 9. Implementing Generalized Panel Data Stochastic Frontier Estimators 225 Subal C. Kumbhakar and Christopher F. Parmeter 1 Introduction 225 2 Earlier Models and Shortcomings 226 3 The Generalized Panel Data Stochastic Frontier Model 230 3.1 Plug-in Estimation 232 3.2 Full Maximum Likelihood 236 3.3 Maximum Simulated Likelihood 239 4 Including Determinants of Inefficiency 240 4.1 Semiparametric Approaches 242 5 Recent Applications of the Generalized Panel Data Stochastic Frontier Model 244 6 Conclusion 246 Acknowledgment 246 References 246 10. Panel Cointegration Techniques and Open Challenges 251 Peter Pedroni 1 Introduction 251 2 Cointegration and the Motivation for Panels 254 3 Strategies for Treating Cross-sectional Heterogeneity in Cointegration Testing and Inference 258 4 Treating Heterogeneity in Residual Based Tests for Cointegration 259 5 Comparison of Residual Based and Error Correction Based Testing 263 6 Estimation and Testing of Cointegrating Relationships in Heterogeneous Panels 266 7 Testing Directions of Long-Run Causality in Heterogeneous Cointegrated Panels 270 xii Contents 8 Strategies for Treating Cross-Sectional Dependence in Heterogeneous Panels 274 9 A Nonparametric Rank Based Approach to Some Open Challenges 277 10 New Directions and Challenges for Nonlinear and Time Varying Long-Run Relationships 282 References 286 11. Alternative Approaches to the Econometrics of Panel Data 289
Recommended publications
  • Crossmedia Adaptation and the Development of Continuity in the Dc Animated Universe
    “INFINITE EARTHS”: CROSSMEDIA ADAPTATION AND THE DEVELOPMENT OF CONTINUITY IN THE DC ANIMATED UNIVERSE Alex Nader A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of MASTER OF ARTS May 2015 Committee: Jeff Brown, Advisor Becca Cragin © 2015 Alexander Nader All Rights Reserved iii ABSTRACT Jeff Brown, Advisor This thesis examines the process of adapting comic book properties into other visual media. I focus on the DC Animated Universe, the popular adaptation of DC Comics characters and concepts into all-ages programming. This adapted universe started with Batman: The Animated Series and comprised several shows on multiple networks, all of which fit into a shared universe based on their comic book counterparts. The adaptation of these properties is heavily reliant to intertextuality across DC Comics media. The shared universe developed within the television medium acted as an early example of comic book media adapting the idea of shared universes, a process that has been replicated with extreme financial success by DC and Marvel (in various stages of fruition). I address the process of adapting DC Comics properties in television, dividing it into “strict” or “loose” adaptations, as well as derivative adaptations that add new material to the comic book canon. This process was initially slow, exploding after the first series (Batman: The Animated Series) changed networks and Saturday morning cartoons flourished, allowing for more opportunities for producers to create content. References, crossover episodes, and the later series Justice League Unlimited allowed producers to utilize this shared universe to develop otherwise impossible adaptations that often became lasting additions to DC Comics publishing.
    [Show full text]
  • Batman: Bloom Vol 9 Free Ebook
    FREEBATMAN: BLOOM VOL 9 EBOOK Greg Capullo,Scott Snyder | 200 pages | 27 Dec 2016 | DC Comics | 9781401269227 | English | United States BATMAN VOL. 9: BLOOM As the new Batman, former police commissioner Jim Gordon is in for the fight of his life against the bizarre threat of Mr. Bloom, who controls the underworld in Gotham City! At the same time, an amnesiac Bruce Wayne has discovered the truth of his past as the Dark Knight—and now, he must descend into the Batcave and reclaim that painful legacy. Bloom and his minions? The penultimate chapter to Scott Snyder and Greg Capullo's epic #1 NEW YORK TIMES bestselling series is here in BATMAN VOL. 9. has been visited by 1M+ users in the past month. Batman: Bloom (Collected) Bloom and his minions? The penultimate chapter to Scott Snyder and Greg Capullo's epic #1 NEW YORK TIMES bestselling series is here in BATMAN VOL. 9. Buy Batman TP Vol 9 Bloom 01 by Snyder, Scott, Capullo, Greg (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on. Issues Batman (Volume 2)#46, Batman (Volume 2)#47, Batman (Volume 2)#48, Batman Batman: Bloom. Cover Batman September, Rated T for Teen (12+) ISBN. Batman Vol. 9: Bloom (the New 52) Buy Batman TP Vol 9 Bloom 01 by Snyder, Scott, Capullo, Greg (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on. Batman Vol. 9: Bloom (The New 52): Snyder, Scott, Capullo, Greg: Books - Download at: ?book= Batman Vol. 9: Bloom (The New 52) pdf download.
    [Show full text]
  • JEROME P. REITER Department of Statistical Science, Duke University Box 90251, Durham, NC 27708 Phone: 919 668 5227
    JEROME P. REITER Department of Statistical Science, Duke University Box 90251, Durham, NC 27708 phone: 919 668 5227. email: [email protected]. September 26, 2021 EDUCATION Ph.D. in Statistics, Harvard University, 1999. A.M. in Statistics, Harvard University, 1996. B.S. in Mathematics, Duke University, 1992. DISSERTATION \Estimation in the Presence of Constraints that Prohibit Explicit Data Pooling." Advisor: Donald B. Rubin. POSITIONS Academic Appointments Professor of Statistical Science and Bass Fellow, Duke University, 2015 - present. Mrs. Alexander Hehmeyer Professor of Statistical Science, Duke University, 2013 - 2015. Mrs. Alexander Hehmeyer Associate Professor of Statistical Science, Duke University, 2010 - 2013. Associate Professor of Statistical Science, Duke University, 2009 - 2010. Assistant Professor of Statistical Science, Duke University, 2006 - 2008. Assistant Professor of the Practice of Statistics and Decision Sciences, Duke University, 2002 - 2006. Lecturer in Statistics, University of California at Santa Barbara, 2001 - 2002. Assistant Professor of Statistics, Williams College, 1999 - 2001. Other Appointments Chair, Department of Statistical Science, Duke University, 2019 - present. Associate Chair, Department of Statistical Science, Duke University, 2016 - 2019. Mathematical Statistician (part-time), U. S. Bureau of the Census, 2015 - present. Associate/Deputy Director of Information Initiative at Duke, Duke University, 2013 - 2019. Social Sciences Research Institute Data Services Core Director, Duke University, 2010 - 2013. Interim Director, Triangle Research Data Center, 2006. Senior Fellow, National Institute of Statistical Sciences, 2002 - 2005. 1 ACADEMIC HONORS Keynote talk, 11th Official Statistics and Methodology Symposium (Statistics Korea), 2021. Fellow of the Institute of Mathematical Statistics, 2020. Clifford C. Clogg Memorial Lecture, Pennsylvania State University, 2020 (postponed due to covid-19).
    [Show full text]
  • Module Three, Part One: Panel Data Analysis in Economic Education Research
    MODULE THREE, PART ONE: PANEL DATA ANALYSIS IN ECONOMIC EDUCATION RESEARCH William E. Becker, William H. Greene and John J. Siegfried* As discussed in Modules One and Two, most of the empirical economic education research is based on a “value-added,” “change-score” or a “difference-in-differences” model specifications in which the expected improvement in student performance from a pre-program measure (pretest) to its post-program measurement (posttest) is estimated and studied for a cross section of subjects. Other than the fact that testing occurs at two different points in time, there is no time dimension, as seen in the data sets employed in Modules One and Two. Panel data analysis provides an alternative structure in which measurements on the cross section of subjects are taken at regular intervals over multiple periods of time.i Collecting data on the cross section of subjects over time enables a study of change. It opens the door for economic education researchers to address unobservable attributes that lead to biased estimators in cross-section analysis.ii As demonstrated in this module, it also opens the door for economic education researchers to look at things other than test scores that vary with time. This module provides an introduction to panel data analysis with specific applications to economic education. The data structure for a panel along with constant coefficient, fixed effects and random effects representations of the data generating processes are presented. Consideration is given to different methods of estimation and testing. Finally, as in Modules One and Two, contemporary estimation and testing procedures are demonstrated in Parts Two, Three and Four using LIMDEP (NLOGIT), STATA and SAS.
    [Show full text]
  • Teen Titans Changing of the Guard Free
    FREE TEEN TITANS CHANGING OF THE GUARD PDF Sean McKeever | 192 pages | 25 Aug 2009 | DC Comics | 9781401223090 | English | New York, NY, United States Teen Titans () #69 - DC Entertainment Jump to navigation. Teen Titans Go! Now play as the youthful up-and-comers. The Teen Titans are all about proving themselves, and with this set you can save your best cards for when you really need them. The big new focus of the set revolves around Ongoing abilities: Cards that stay in play until you need them. Every time you put an Ongoing card into Teen Titans Changing of the Guard, it essentially gives you an extra card to utilize on a future turn. Previously, only Locations and a couple of other cards could ever stay in play. Now every card type at every power level has multiple different cards that are Ongoing. Sometimes they help you every turn. But mostly they stay in play until Teen Titans Changing of the Guard choose to discard them for their mighty effects. If you can build up several Ongoing cards, unleash as many as you need to take down the Super-Villains! This set also pays attention to the different card types you have in play. Playing cards from your hand puts cards into play as usual. But with Ongoing cards out there, you often have several card types in play already. Such synergy! Contents Summary:. Buy Now. Learn To Play. All Rights Reserved. Teen Titans: Changing of the Guard | DC Database | Fandom Goodreads helps you keep track of books you want to read.
    [Show full text]
  • The Evolution of Batman and His Audiences
    Georgia State University ScholarWorks @ Georgia State University English Theses Department of English 12-2009 Static, Yet Fluctuating: The Evolution of Batman and His Audiences Perry Dupre Dantzler Georgia State University Follow this and additional works at: https://scholarworks.gsu.edu/english_theses Part of the English Language and Literature Commons Recommended Citation Dantzler, Perry Dupre, "Static, Yet Fluctuating: The Evolution of Batman and His Audiences." Thesis, Georgia State University, 2009. https://scholarworks.gsu.edu/english_theses/73 This Thesis is brought to you for free and open access by the Department of English at ScholarWorks @ Georgia State University. It has been accepted for inclusion in English Theses by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. STATIC, YET FLUCTUATING: THE EVOLUTION OF BATMAN AND HIS AUDIENCES by PERRY DUPRE DANTZLER Under the Direction of H. Calvin Thomas ABSTRACT The Batman media franchise (comics, movies, novels, television, and cartoons) is unique because no other form of written or visual texts has as many artists, audiences, and forms of expression. Understanding the various artists and audiences and what Batman means to them is to understand changing trends and thinking in American culture. The character of Batman has developed into a symbol with relevant characteristics that develop and evolve with each new story and new author. The Batman canon has become so large and contains so many different audiences that it has become a franchise that can morph to fit any group of viewers/readers. Our understanding of Batman and the many readings of him gives us insight into ourselves as a culture in our particular place in history.
    [Show full text]
  • Visualizing Research Collaboration in Statistical Science: a Scientometric
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 9-13-2019 Visualizing Research Collaboration in Statistical Science: A Scientometric Perspective Prabir Kumar Das Library, Documentation & Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata - 700108, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons Das, Prabir Kumar, "Visualizing Research Collaboration in Statistical Science: A Scientometric Perspective" (2019). Library Philosophy and Practice (e-journal). 3039. https://digitalcommons.unl.edu/libphilprac/3039 Viisualliiziing Research Collllaborattiion iin Sttattiisttiicall Sciience:: A Sciienttomettriic Perspecttiive Prrabiirr Kumarr Das Sciienttiiffiic Assssiissttantt – A Liibrrarry,, Documenttattiion & IInfforrmattiion Sciience Diiviisiion,, IIndiian Sttattiisttiicall IInsttiittutte,, 203,, B.. T.. Road,, Kollkatta – 700108,, IIndiia,, Emaiill:: [email protected] Abstract Using Sankhyā – The Indian Journal of Statistics as a case, present study aims to identify scholarly collaboration pattern of statistical science based on research articles appeared during 2008 to 2017. This is an attempt to visualize and quantify statistical science research collaboration in multiple dimensions by exploring the co-authorship data. It investigates chronological variations of collaboration pattern, nodes and links established among the affiliated institutions and countries of all contributing authors. The study also examines the impact of research collaboration on citation scores. Findings reveal steady influx of statistical publications with clear tendency towards collaborative ventures, of which double-authored publications dominate. Small team of 2 to 3 authors is responsible for production of majority of collaborative research, whereas mega-authored communications are quite low.
    [Show full text]
  • Rydberg Atoms in an Oscillating Field: Extracting Classical Motion from Quantum Spectra by Neal W
    Rydberg Atoms in an Oscillating Field: Extracting Classical Motion from Quantum Spectra by Neal W. Spellmeyer B.S., University of Wisconsin, Madison (1992) Submitted to the Department of Physics in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 1998 © Massachusetts Institute of Technology 1998. All rights reserved. Signature of Author Department ofPhysics December 5, 1997 Certified by Aaniel Kleppner Lester Wolfe Professor of Physics Thesis Supervisor Accepted by George F. Koster Professor of Physics Chairman, Departmental Committee on Graduate Students Rydberg Atoms in an Oscillating Field: Extracting Classical Motion from Quantum Spectra by Neal W. Spellmeyer Submitted to the Department of Physics on December 5, 1997, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract We present an experimental and theoretical investigation of the connections between classical and quantum descriptions of Rydberg atoms in external electric fields. The technique of recurrence spectroscopy, in which quantum spectra are measured in a manner that maintains constant classical scaling laws, reveals the actions of the closed orbits in the corresponding classical system. We have extended this technique with the addition of a weak oscillating electric field. The effect of this perturbing field is to systematically weaken recurrences in a manner that reveals the ac dipole moments of the unperturbed orbits, at the frequency of the applied field. We outline a version of closed orbit theory developed to describe these experiments, and show that it is in good agreement with the measurements. The experiments also show good agreement with semiquantal Floquet computations.
    [Show full text]
  • RABI BHATTACHARYA the Department of Mathematics the University of Arizona 617 N Santa Rita Tucson, AZ 85721
    RABI BHATTACHARYA The Department of Mathematics The University of Arizona 617 N Santa Rita Tucson, AZ 85721. 1. Degrees Ph.D. (Statistics) University of Chicago, 1967. 2. Positions Held: Regular Positions 1967-72 Assistant Professor of Statistics, Univ. of California, Berkeley. 1972-77 Associate Professor of Mathematics, University of Arizona. 1977-82 Professor, University of Arizona. 1982-02 Professor, Indiana University (Dept. of Mathematics). 2002- Professor of Mathematics and member of BIO5 faculty, University of Arizona. Visiting Positions 1978-79 Visiting Professor, Indian Statistical Institute. 1979 (Summer) Distinguished Visiting Professor, Math. and Civil Engi- neering, University of Mississippi. 1992 Visiting Professor, University of Bielefeld, Germany, June 26-July 28. 1994 Visiting Professor, University of Bielefeld, Germany, Feb. 15-April 15. 1994 Visiting Professor, University of G¨ottingen, Germany, April 15-June 15. 1996 Visiting Professor, University of Bielefeld, Germany, June 8-July 9. 2000 Visiting Scholar, Stanford University, June-September. 2000-01 Visiting Scholar, Oregon State University. 1 3. Honors and Awards: 1977 Special Invited Paper, Annals of Probability 1978 IMS Fellow 1989 DMV Lecturer (by invitation of the German Mathematical Society) 1994-95 Humboldt Prize (Alexander von Humboldt Forschungspreis) 1996 IMS Special Invited Lecture, IMS Annual Meeting, Chicago (Medal- lion Lecture) 1999 Special Invited Paper, Annals of Applied Probability 2000 Guggenheim Fellowship 4. Professional Associations: Fellow of Institute of Mathematical Statistics American Mathematical Society 5. Research Interests: 1. Markov processes in discrete and continuous time and stochastic differen- tial equations, and their applications to equations of mathematical physics, science, engineering and economics. 2. Analytical theory and refinements of central limit theorems.
    [Show full text]
  • Ronald Christensen
    Ronald Christensen Professor of Statistics University of New Mexico Department of Mathematics and Statistics EDUCATION: Ph.D., Statistics, University of Minnesota, 1983. M.S., Statistics, University of Minnesota, 1976. B.A., Mathematics, University of Minnesota, 1974. EXPERIENCE: 1994- Professor, Department of Mathematics and Statistics, University of New Mexico. 1998-2001, 2003- Founding Director, Statistics Clinic, University of New Mexico. 1988-1994 Associate Professor, Department of Mathematics and Statistics, University of New Mexico. 1982-1988 Assistant Professor, Department of Mathematical Sciences, Montana State Uni- versity. 1994 Visiting Professor, Department of Mathematics and Statistics, University of Canterbury, Chch., N.Z. 1987 Visiting Assistant Professor, Department of Theoretical Statistics, University of Minnesota. RESEARCH INTERESTS: Linear Models Bayesian Inference Log-linear and Logistic Regression Models Statistical Methods SOCIETIES AND HONORS: Fellow of the American Statistical Association, 1996 Fellow of the Institute of Mathematical Statistics, 1998 International Biometric Society American Society for Quality Phi Beta Kappa 1 PUBLICATIONS: 1. Christensen, Ronald (2007). \Notes on the decompostion of effects in full factorial experimental designs." Quality Engineering, accepted. (Letter.) 2. Yang, Mingan, Hanson, Timothy, and Christensen, Ronald (2007). \Nonparametric Bayesian estimation of a bivariate density with interval censored data." Statistics in Medicine, under review. 3. Christensen, Ronald (2007). \Discussion: Dimension reduction, nonparametric regres- sion, and multivariate linear models." Statistical Science, accepted. 4. Christensen, Ronald and Johnson, Wesley (2007). \A Conversation with Seymour Geisser." Statistical Science, accepted. 5. Christensen, Ronald (2006). \Comment on Monahan (2006)." The American Statisti- cian, 60, 295. 6. Christensen, Ronald (2006). \General prediction theory and the role of R2." The American Statistician, under revision.
    [Show full text]
  • Lectures On: Panel Data Analysis for Social Scientists, Given at the University of Bergen, October 2006
    Lectures on: Panel data analysis for social scientists, given at the University of Bergen, October 2006 You may find these lecture notes a useful complement to those I will use for EC968. They cover a wider range of topics and go at a slower pace, with less emphasis on technical issues. Steve Pudney Panel Data Analysis for Social Scientists University of Bergen Department of Sociology Department of Comparative Politics Steve Pudney Gabriella Conti ISER Aims of course • Introduce the distinctive features of panel data. • Review some panel data sets commonly used in social sciences. • Present the advantages (and limitations) of panel data, and consider what sort of questions panel data can(not) address. • Show how to handle and describe panel data. • Introduce the basic estimation techniques for panel data • Discuss how to choose (and test for) the right technique for the question being addressed. • Discuss interpretation of results 01/02/2007 (2) Structure of course (1) • 5 days × (3 hours lectures + 2 hour lab sessions ) • Lab sessions will illustrate concepts using Stata software (“industry standard” in survey-based applied work) • Main data will be from British Household Panel Survey (BHPS) • Focus is on understanding the concepts and applying them. • Full lecture slides on the web • Technical detail kept to a minimum but available in “appendices” 01/02/2007 (3) Structure of course (2) Day 1: Basics • What are panel data (examples)? • Why use panel data? • Handling panel data in Stata – some basic commands. • Patterns of observations in panel data (non-response and attrition) • Within and between variation • Transitions. • Cohort analysis Day 2: Statistical analysis • Inference using panel data: some identification issues unobservables.
    [Show full text]
  • Abbreviations of Names of Serials
    Abbreviations of Names of Serials This list gives the form of references used in Mathematical Reviews (MR). ∗ not previously listed The abbreviation is followed by the complete title, the place of publication x journal indexed cover-to-cover and other pertinent information. y monographic series Update date: January 30, 2018 4OR 4OR. A Quarterly Journal of Operations Research. Springer, Berlin. ISSN xActa Math. Appl. Sin. Engl. Ser. Acta Mathematicae Applicatae Sinica. English 1619-4500. Series. Springer, Heidelberg. ISSN 0168-9673. y 30o Col´oq.Bras. Mat. 30o Col´oquioBrasileiro de Matem´atica. [30th Brazilian xActa Math. Hungar. Acta Mathematica Hungarica. Akad. Kiad´o,Budapest. Mathematics Colloquium] Inst. Nac. Mat. Pura Apl. (IMPA), Rio de Janeiro. ISSN 0236-5294. y Aastaraam. Eesti Mat. Selts Aastaraamat. Eesti Matemaatika Selts. [Annual. xActa Math. Sci. Ser. A Chin. Ed. Acta Mathematica Scientia. Series A. Shuxue Estonian Mathematical Society] Eesti Mat. Selts, Tartu. ISSN 1406-4316. Wuli Xuebao. Chinese Edition. Kexue Chubanshe (Science Press), Beijing. ISSN y Abel Symp. Abel Symposia. Springer, Heidelberg. ISSN 2193-2808. 1003-3998. y Abh. Akad. Wiss. G¨ottingenNeue Folge Abhandlungen der Akademie der xActa Math. Sci. Ser. B Engl. Ed. Acta Mathematica Scientia. Series B. English Wissenschaften zu G¨ottingen.Neue Folge. [Papers of the Academy of Sciences Edition. Sci. Press Beijing, Beijing. ISSN 0252-9602. in G¨ottingen.New Series] De Gruyter/Akademie Forschung, Berlin. ISSN 0930- xActa Math. Sin. (Engl. Ser.) Acta Mathematica Sinica (English Series). 4304. Springer, Berlin. ISSN 1439-8516. y Abh. Akad. Wiss. Hamburg Abhandlungen der Akademie der Wissenschaften xActa Math. Sinica (Chin. Ser.) Acta Mathematica Sinica.
    [Show full text]