Global School in Empirical Research Methods by Universidad EAFIT

Global School 3 - 1 4 D E C E M B E R 2 0 1 8 - M E D E L L Ă? N , C O LO M B I A

Global School in Empirical Research Methods (GSERM) at Universidad EAFIT, 2018

MEDELLÍN Medellín is a dynamic metropolis that has undergone a cultural, social, and economic transformation in recent years. Colombia’s second city both economically and in terms of number of inhabitants, it is located 1,538 meters (5,046 feet) above sea level, boasts a temperate climate with year-round temperatures ranging from between 18 and 28 degrees Celsius (64 and 82 degrees Fahrenheit), and has a history dating back nearly 340 years. The Medellín metropolitan area, which comprises 10 municipalities located in the Aburrá Valley, currently is home to roughly 3,800,000 people. This urban conglomerate in northwestern Colombia (nestled amid the Andes’ Cordillera Central range) hosts world-class events such as the Flower Festival, the International Poetry Festival, the Medellín Book and Culture Festival, Colombiamoda, and Colombiatex.

EAFIT EAFIT is a university with 57 years of history that inspires the current generations to embark on a life project that maximizes their potential. An institution that both conveys and generates knowledge, thus fulfilling its role as a teaching and research university. A place that transforms society. EAFIT currently offers 22 undergraduate degree programs, nearly 50 graduate certificate programs, 34 master’s degree programs, and 6 doctorate programs in the schools of Management, Engineering, Humanities, Law, Economics and Finance, and Sciences, and it has been recognized three times with the Institutional Accreditation conferred by the National Education Ministry MEN. (2003, 2010 and 2018). Research-backed teaching is a focal point of the institution’s road map, in so far as the goal is to not only convey but also generate knowledge. In that regard, the budget allocated to this area has been maintained and enabled EAFIT to achieve a range of objectives, including recognition by Colciencias of 41 research groups, the registration of 37 patents, and the participation of more than 1,170 students in research incubators. The institution’s reach also extends to other publics and in other parts of Colombia like EAFIT Bogotá, EAFIT Pereira, and EAFIT Llanogrande. All of these processes take place on a campus where the built infrastructure coexists with different species of flora and fauna to make a university-park equipped with cutting-edge technology. Music, literature, dance, theater, film cycles, exhibitions, and other artistic manifestations make up a cultural agenda that permeates all parts of campus, enables the formation of groups with shared interests, and fosters the organization’s reach and impact both externally and internally. And it is in this context, in which the university strives to give back all it has received from society, that its social responsibility efforts are infused with a greater sense of purpose. With these strategic commitments in mind, EAFIT is firm in its promise to inspire, create and transform. 4

COURSES 1ST SESSION: 3-7 DECEMBER 2018 Instructor

Course

Date

Level

ECTS

Michael Lechner

Causal Inference

3 - 7 December 2018

Brett Lantz

Machine Learning with R – Introduction

3 - 7 December 2018

Andrew Bennett

Case Study Methods

3 - 7 December 2018

2ND SESSION: 10-14 DECEMBER 2018 Instructor

Course

Date

Level

ECTS

Carsten Q. Schneider

Qualitative Comparative Analysis

10 - 14 December 2018

Sarah K. Jacobson Sera Linardi

Experimental Methods for Behavioral Science

10 - 14 December 2018

Christian B. Hansen

Econometrics of Big Data

10 - 14 December 2018

B = Basic | M = Intermediate | A = Advanced | R = Research Courses start at CHF 1000 (approx. USD 1050) | Deadline for application is 15 November 2018.

PRICING PROGRAMME FEE 1 course (1 week) 2 courses (2 weeks)

CHF 1000 (approx. USD 1’050) CHF 1900 (approx. USD 1’995)

EARLY BIRD DISCOUNT UNTIL 3O SEPTEMBER 2018 Discount for 1 course CHF 200 (~ USD 210) Discount for 2 courses CHF 300 (~ USD 315)

DESCRIPTION MACHINE LEARNING WITH R-INTRODUCTION

CAUSAL INFERENCE Modern microeconometrics has the goal of empirically establishing and quantifying causal relations between interesting variables, such as participating in some government intervention and individual labour market outcomes. In this course we discuss the most relevant methods (also called research designs in microeconometrics) that are used

in empirical practice, such as matching, instrumental variables, regression discontinuity designs as well as difference-in-difference estimation based on cross-sectional and panel data. The methods will be explained together with their potential virtues and limitations in different disciplines. For each of the different research designs we start with a general discussion of its key assumptions and how they relate to empirical settings. Next,

we discuss estimation principles as well as particular estimators suitable for the context of the particular design. Finally, some time will be devoted to look in more detail in an empirical paper that uses the particular methods. These empirical papers will come from the fields of labour, health and sports economics. Usually, in the morning we discuss the theory while the afternoon is devoted to empirical applications.

Machine learning, put simply, involves teaching computers to learn from experience, typically for the purpose of identifying or responding to patterns or making predictions about what may happen in the future. This course is intended to be an introduction to machine learning methods through the exploration of real-world examples. We will cover the basic math and statistical theory needed to understand and apply many of the

most common machine learning techniques, but no advanced math or programming skills are required. The target audience may include social scientists or practitioners who are interested in understanding more about these methods and their applications. Students with extensive programming or statistics experience may be better served by a more theoretical course on these methods.

QUALITATIVE COMPARATIVE ANALYSIS

CASE STUDY METHODS The course is designed for Master, PhD students and practitioners in the social and policy sciences, including political science, sociology, public policy, public administration, business, and economics. Previous courses in research methods and

philosophy of science are helpful but not required. Materials not in the books assigned for purchase and not easily available through online library databases will be made available electronically. Bringing a laptop to class will be helpful but is not essential.

This course introduces participants to set-theoretic methods and their application in the social sciences with a focus on Qualitative Comparative Analysis. The course starts out by familiarizing students with the basic concepts of the underlying methodological perspective, among them the central notions of necessity and sufficiency, formal logic and Boolean algebra. From there, we move to the logic and analysis of truth tables

and discuss the most important problems that emerge when this analytical tool is used for exploring social science data. Right from the beginning, students will be exposed to performing set-theoretic analyses with the relevant R software packages. When discussing settheoretic methods, in-class debates will engage on broad, general comparative social research issues, such as case selection principles, concept formation, questions of

data aggregation and the treatment of causally relevant notions of time. Examples are drawn from published applications in the social sciences. Participants are encouraged to bring their own raw data for in-class exercises and assignments, if available. By the end of the course, participants will be able to perform set-theoretic analyses of their own and to critically evaluate published QCA.

EXPERIMENTAL METHODS FOR BEHAVIORAL SCIENCE

ECONOMETRICS OF BIG DATA

This course provides an introduction to the use of experiments in the social sciences. Well-designed experiments, in both the lab and the field, provide the control necessary to identify causal relationships and to cleanly test theoretical constructs. Experiments are an important part of the social science toolkit, and are becoming increasingly indispensable parts of the methodological toolkit in economics, political science, and other disciplines.

As in many other fields, economists are increasingly making use of high-dimensional models â&#x20AC;&#x201C; models with many unknown parameters that need to be inferred from the data. Such models arise naturally in modern data sets that include rich information for each unit of observation (a type of â&#x20AC;&#x153;big dataâ&#x20AC;?) and in nonparametric applications where researchers wish to learn, rather than impose, functional forms. High-dimensional models

Students in this course will develop an understanding of the elements of good experimental design and implementation and examine the interaction between theory and experimental design. Students will further advance their knowledge through experience: they will work individually or in teams throughout the course to develop and pilot their own experiment design, and they will receive feedback from instructors and fellow students.

Topics covered during this course include: design and implementation of experiments, field and internet experiments, classic games, and individual preference experiments (risk, time, social). We will use studies in specific topic areas (environmental and natural resource applications, and social policy, especially in interventions targeted to the poor) to demonstrate central concepts in social science experimentation.

provide a vehicle for modeling and analyzing complex phenomena and for incorporating rich sources of confounding information into economic models. Our goal in this course is two-fold. First, we wish to provide an overview and introduction to several modern methods, largely coming from statistics and machine learning, which are useful for exploring high-dimensional data and for

building prediction models in highdimensional settings. Second, we will present recent proposals that adapt high-dimensional methods to the problem of doing valid inference about model parameters and illustrate applications of these proposals for doing inference about economically interesting parameters.

INSTRUCTORS MICHAEL LECHNER Michael Lechner works as professor of Econometrics at the University of St. Gallen since 1998. In 1994, he received his PhD in Economics and Econometrics at the University of Mannheim. He co-heads the Swiss Institute for Empirical Economic Research (SEW). He is interested in the evaluation of labor market programmes, sports economics, and the development of microeconometric methods for causal inference and their link to machine learning. He has published in the Journal of Econometrics, Journal of the American Statistical Association, Journal of the European Economic Association, the European Economic Review, and the Journals of Labor Economics, of Health Economics and of Human Resources among many others. He is a Fellow of the German Academy of Science Leopoldina (Halle), and the Center for Economic Policy Reseach (CEPR, London).

BRETT LANTZ Brett Lantz is a data scientist at the University of Michigan and the author of Machine Learning with R, a best-selling textbook praised for its beginner-friendly practical approach to the topic. After studying sociology and machine learning at the University of Michigan (B.S.) and University of Notre Dame (M.A.), Brett has spent more than 10 years using innovative data methods to understand human behavior. First captivated by machine learning while studying a large database of teenagersâ&#x20AC;&#x2122; social network profiles, Brett has since worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others.

ANDREW BENNETT Andrew Bennett is Professor in the Department of Government at Georgetown University. He has worked in the U.S. Defense Department and the U.S. Senate, and he has written on the U.S. foreign policy process, case study research methods, alliance burden‐sharing, and regional conflicts and peacekeeping. He is, with Alexander George, the co‐author of Case Studies and Theory Development in the Social Sciences (MIT Press, 2005). Together with Colin Elman and David Collier, he is a founder of the Institute for Qualitative and Multimethod Research, now at Syracuse University, and he has taught case study research methods in Argentina, Chile, Germany, Norway, Sweden, and Switzerland.

CHRISTIAN B. HANSEN Christian B. Hansen studies applied and theoretical econometrics, the uses of high-dimensional statistical methods in economic applications, estimation of panel data models, quantile regression, and weak instruments. In 2008, Hansen was named a Neubauer Family Faculty Fellow, and he was named to the Wallace W. Booth professorship in 2014. Hansen graduated from Brigham Young University with a bachelor’s degree in economics in 2000. In 2004, he received a PhD in economics from the Massachusetts Institute of Technology, where he was a graduate research fellow of the National Science Foundation. He joined the Chicago Booth faculty in 2004.

SARAH K. JACOBSON Sarah Jacobson is an Associate Professor of Economics at Williams College, where she has been since 2010. She completed her PhD in Economics at Georgia State University in 2010 and her Bachelor of Science in Engineering at Harvey Mudd College in 1998. She is an environmental and behavioral economist who studies interactions between preferences and institutions, mostly using laboratory experiments. Her work has been published in top field journals such as Journal of Environmental Economics and Management and Experimental Economics. Specific themes in her work include reciprocity, cases in which social preferences yield inefficient outcomes, and punishment and deterrence. She has current projects on security institutions, proxy decision-making, the enforcement of water pollution regulations, racial bias, and charity donations.

SERA LINARDI Sera Linardi is an Associate Professor at the Graduate School of Public and International Affairs (GSPIA) at the University of Pittsburgh. She received her PhD in Social Science at the California Institute of Technology after working as a computer scientist at Adobe Systems. She bridges academic research and practical challenges in public/social services provision, specifically around prosocial behavior, information aggregation, and behavior economics of the poor. Her research has been published in both economics and political science journals (Journal of Public Economics, Management Science, Games and Economic Behavior, British Journal of Political Science) and won the 2016 MPSA Best Paper in Comparative Politics Award. Her work is currently supported by the National Science Foundation (NSF) and Heinz Endowment.

CARSTEN Q. SCHNEIDER Carsten Q. Schneider is Professor of the Political Science Department at Central European University (CEU) in Budapest, Hungary. Prior to joining CEU in 2004, he obtained his PhD from the European University Institute in Florence. His research focuses on regime transitions, the consolidation and quality of democracies. He is also working in the field of comparative methodology, especially on set-theoretic methods, in particular Qualitative Comparative Analysis (QCA) and its fuzzy set extension. His book “SetTheoretic Methods for the Social Sciences”, co-authored with Claudius Wagemann, appeared with Cambridge University Press in 2012. From 2009 – 2014, Schneider was an elected member of the Young Academy of Science in Germany (http://www.diejungeakademie.de/) and he spent the Academic Year 2009-2010 as a John F. Kennedy Memorial Fellow at the Minda de Gunzburg Center for European Studies at Harvard University (http://www. ces.fas.harvard.edu/). As its Founding Director, Schneider led the Center for the Study of Imperfections in Democracies (DISC) at CEU from 2008 to 2014.

COURSE STRUCTURE GSERM is structured in different modules. Participants select one course per week as these courses are taught concurrently. GSERM courses are awarded 4 ECTS credits if exams/homework are successfully passed. GSERM courses are divided into different levels: basic, intermediate, advanced and research. Please pay careful attention to indicated levels in the course directory. Courses on research level (PEF-courses) are especially designed for PhD students in Economics with substantial background in quantitative methods. If you successfully pass the exam you will receive an official University of St. Gallen transcript about 10 weeks after the exam. If you do not take the exam you may send us a request for a participation certificate.

RESEARCH

ADVANCED INTERMEDIATE

BASIC WORKSHOP LECTURES

SCHEDULE CALENDAR Lunes, 3 de diciembre de 2018 08:00

Welcoming session and Registration.