The Quantitative Methods and Social Analysis (QMSA) concentration in the MA Program in the Social Sciences (MAPSS) at the University of Chicago prepares a new generation of scholars to innovate methodologically and to use the theory of statistical ifnerence to tackle challenging problems in a wide range of research such as poverty, crime, health disparity, public opinions, political participation, human develoment, cognition and emotions, genes and environment, and knowledge diffusion.

The QMSA concentration is led by the Committee on Quantitative Methods in Social, Behavioral, and Health Sciences, a University-wide interdisciplinary group of distinguished scholars who share the intellectual focus on developing innovative methods and incorporating new technology for advancing theor-driven and data-informed rigorous research on populations, societies, and behavior. The members include faculty from departments in the Division of Social Sciences (Comparative Human Development, Economics, Political Science, Psychology, and Sociology), Biological Sciences (Human Genetics, Medicine, and Public Health Sciences), The Harris School of Public Policy, and the Department of Statistics. Our faculty are working at th cutting-edge of quantitative methods including but not limited to geo-spatial modeling, intensive longitudinal data analysis, multilevel modeling, network analysis, causal inference, econometrics, demographic techniques, measurement, survey methodology, innovative research designs, machine learning, and content analysis.

QMSA is a one-year program with 9 graduate-level courses over three academic quarters, culminating in an article-length MA thesis, with a primary goal of preparing students for PhD in quantitative social sciences. QMSA students receive rigorous education in statistical theory, advanced quantitative methods, and social scientific research and are mentored one-on-one by a Committee faculty member in developing a thesis project. Students may participate in ongoing research or conduct independent projects applying advanced quantitative thinking and analysis to a significant research question. In addition, QMSA students join a biweekly workshop with faculty, postdoctoral researchers, and doctoral students as well as renowned scholars from around the country. QMSA students also find easy access to many other interdisciplinary or disciplinary workshops, colloquia, and seminars on the campus. After earning the degree, graduates join research teams at UChicago or seek professional experiences in research institutions and government or non government agencies as well as pursuing doctoral training.

## QMSA Courses

MAPSS Preceptor for 2018-2019: Muh-Chung Lin, muhchung@outlook.com

**SOCI 30253 Introduction to Spatial Data Science**

Spatial Data Science is an evolving field that can be thought of as a collection of concepts and methods drawn from both statistics and computer science. These techniques deal with accessing, transforming manipulating, visualizing, exploring and reasoning about data where the locational component is important (spatial data). The course introduces the types of of spatial data relevant in social science inquiry and reviews a range of methods to explore these data. **Prerequisite**: STAT 22000 (or equivalent), familiarity with GIS is helpful, but not necessary

**SOCI 40217 Spatial Regression Analysis**

This course covers statistical and econometric methods specifically geared to deal with the problems of spatial dependence and spatial heterogeneity in cross-sectional and panel (space-time) data. The main objective of the course is to gain insight into the scope of spatial regression methods, to be able to apply them in an empirical setting, and to properly interpret the results of spatial regression analysis. While the focus is on spatial aspects, the types of methods covered have general validity in statistical practice. The course covers specification of spatial regression models in order to incorporate spatial dependence and spatial heterogeneity, as well as different estimation methods and specification tests to detect the presence of spatial autocorrelation and spatial heterogeneity.**Prerequisite**: Graduate level econometrics or multivariate regression, matrix algebra

**PPHA 41400 Applied Regression Analysis: Analysis of Microeconomic Data**

This course is based on the theory and practice of econometrics. Its intention is to provide hands-on experience with econometric analysis, without neglecting sound knowledge of econometric theory. It is designed to help students acquire skills that make them effective consumers and producers of empirical research in public policy, economics and related fields. Throughout the course, concepts will be illustrated with application in economics. Various aspects will be covered in the course, in particular: I) Development of testable econometric models; II) Use of appropriate data, and; III) specification and estimation of econometric models.**Prerequisites**: STAT 22400 Applied Regression Analysis or equivalent

**STAT 22400 Applied Regression Analysis**

This course is an introduction to the methods and applications of fitting and interpreting multiple regression models. The main emphasis is on the method of least squares. Topics include the examination of residuals the transformation of data, strategies and criteria for the selection of a regression equation, the use of dummy variables, tests of it. Stata computer package will be used extensively, but previous familiarity with Stata is not assumed. The techniques discussed will be illustrated by real examples involving biological and social science data. **Prerequisites**: HSTD32100; STAT22000 or STAT23400 or equivalent.

**PBHS 33500 Statistical Applications**

This course provides a transition between a statistical applications in medicine, mental health, environmental science, analytical chemistry, and public policy. Lectures are oriented around specific examples from a variety of content areas. Opportunities for the class to work on interesting applied problems presented by U of C faculty will be provided. Although an overview of relevant statistical theory will be presented, emphasis is on the development of statistical solutions to interesting applied problems. **Prerequisites**: PBHS 32700/ Statistics 22700 (Biostatistical Methods) or Statistics 34700 (Generalized Linear Models); or permission of instructor. Facility with at leasts one of the following software packages is assumed: SAS, "R," Stata, or SuperMix.

**PPHA 34600 Program Evaluation Section I**

To introduce students to program evaluation and provide an overview of current issues and methods for estimating treatment impacts. **Prerequisites**: PPHA31000 and PPHA31100 or equivalent coursework in statistics. Students lacking these prerequisites should seek permission from the instructor.

**PPHA 34600 Program Evaluation Section II**

To introduce students to program evaluation and provide an overview of current issues and methods for estimating treatment impacts.**Prerequisites**: PPHA31000 and PPHA 31100 or equivalent coursework in statistics. Students lacking these prerequisites should seek permission from the instructor.

**PPHA 42000 Applied Econometrics I**

This class covers basic Gauss-Markov theory and some extensions. Think of it as a theoretical course for applied researchers. **Prerequisites**: This course is intended for first or second-year Ph.D. students or advanced masters-level students who have take the Statistics 24400/24500 sequence.

**ECON 31100 Empirical Analysis II**

This course develops and applies time series statistical methods for the analysis of dynamic economic models. It develops tools from probability theory, statistics and decision theory and uses them to study linear and nonlinear models of economic time series. These tools support applications in empirical macroeconomics and finance.**Prerequisites**: ECON 31000 Empirical Analysis I

**ECON 31200 Empirical Analysis III**

The course will review some of the classical methods you were introduced to in previous quarters and give examples of their use in applied microeconomic research. Our focus will be on exploring and understanding data sets, evaluating predictions of economic models, and identifying and estimating the parameters of economic models. The methods we will build on include regression techniques, maximum likelihood, method of moments estimators, as well as some non-parametric methods. Lectures and homework assignments will seek to build proficiency in the correct application of these methods to economic research questions. **Prerequisites: **ECON 31100 Empirical Analysis II

**ECON 34930 Inequality, Theory, Methods & Evidence**

This is a graduate seminar designed to investigate basic issues in the study of inequality and social mobility. It is more than a readings course. It is designed to go into topics in depth. Students (individuals or groups off students) will discuss a topic in depth for the entire group. The instructors will work with the students in advance of their presentations, reviewing and participating in the formal presentations. Theory, methods, and evidence will be synthesized. Discussions of topics may straddle class meetings as required. Students and faculty will choose topics of mutual interest. Some outside visitors will coordinate discussions. **Prerequisites**: Graduate level course in micro and econometrics.

**PBHS 33300 Applied Longitudinal Analysis**

Longitudinal data consist of mutiple measures over time on a sample of individuals. These types of data occur extensively in both observational and experimental biomedical and public health studies, as well as in studies in sociology and applied economics. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Whereas some supporting statistical theory will be given, emphasis will be on data and interpretation of models for longitudinal data. Problems will be motivated by applications primarily in mental health, public health, prevention research, and health services research. **Prerequisites**: PBHS 32400/STAT 22400 (Applied Regression Analysis) or equivalent, and PBHS 32600/STAT 222600 (Analysis of Categorical Data) or PBHS 32700/STAT 22700 (Biostatistical Methods) or equivalent; or consent of instructor. Some facility with Stata is assumed.

**BUS 41201 Big Data**

Students will learn how to explore and analyze large high-dimensional datasets, become adept at building powerful systems for prediction, and gain the understanding necessary for interpreting structure in such models. This course includes the key concepts and tools that data scientists find valuable in business environments, and it is also designed to act as primer for continued study. It is not specifically an introduction to computer sciences or machine learning, nor a class on high-dimensional econometrics and statistics; rather like a good data scientist, the class borrows from multiple disciplines. **Prerequisites**: BUS 41000 or 41100. Prerequisite materials includes fundamentals of probability and the following: random variables (and functions thereof), normal and multinomial distributions, confidence/prediction interals, hypothesis testing and sampling distributions. In particular, you should be comfortable with the basics of linear regression as covered in 41000 or 41100.

**CHDV 20101/30101 Applied Statistics in Human Development Research**

This course provides an introduction to quantitative methods of inquiry and a foundation for more advanced courses in applied statistics for students in social sciences with a focus on human development research. The course covers univariate and bivariate descriptive statistics, and introduction to statistical inference, *t* test, two-way contingency table, analysis of variance, and regression. All statistical concepts and methods will be illustrated with application studies in which we will consider the research questions, study design, analytical choices, validity of inferences, and reports of findings. The examples include (1) examining the relationship between home environment and child development, (2) evaluating the effectiveness of welfare-to-work programs on maternal and child well-being, and (3) assessing the academic growth of English language learners in comparison with their English-speaking peers. At the end of the course, students should be able to define and use the descriptive and inferential statistics taught in this course to analyze data and to interpret the analytical results. Students will learn to use the SPSS software. No prior knowledge in statistics is assumed. **Prerequisites**: High school algebra and probability are the only mathematical pre-requisites.

**CHDV 30102 Introduction to Causal Inference**

This course is designed for graduate students and advanced undergraduate students from the social sciences, education public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. The goal of this course is to equip students with basic knowledge of and analytic skills in causal inference. Topics for this course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods incuding matching, stratification, inverse-probability-of-treatment-weighting (IPTW). marginal mean weighting through stratificaion (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RDD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-sectional and panel data, and fixed effects model. **Prerequisites**: Intermediate Statistics or equivalent such as STAT 22400/PBHS 32400, PPHA 31301, BUS 41100, or SOC 30005 is a prerequisite.

**CHDV 32411 Mediation, Moderation and Spillover Effects **

This course is designed for graduate students and advanced undergraduate students from social sciences, statistics, public health science, public policy, and social services administration who will be or are currently involved in quantitative research. Questions about why a treatment works, for whom, under what conditions, and whether on individual's treatment could affect other individuals' outcomes are often key to the advancement of scientific knowledge. We will clarify the theoretical concepts of mediated effects, moderated effects, and spillover effects under the potential outcomes framework. The course introduces cutting-edge methodological approaches and contrasts them with conventional strategies including multiple regression, path analysis, and structural equation modeling. The course content is organized around application examples. The textbook "Causality in a Social World: Moderation, Mediation and Spill-Over" (Hong, 2015) will be supplemented with other reading reflecting latest developments and controversies. **Prerequisites**: Intermediate Statistics such as STAT 22400/PBHS 32400, PPHA 31301, BUS 41100: Applied Regression Analysis, or SOCI 30005: Statistical Methods of Research 2 and Introduction to Causal Inference or their equivalent are prerequisites.

**PLSC 29101 Game Theory I**

This course is an introduction to game theory. It serves a prerequisite (covering games of complete information) to Game Theory II (covering games of incomplete information) offered in the Winter quarter. The origins of game theory reach back to beginning of the 20th century when John Von Neumann paired up with Oscar von Morgenstern to write the "Theory of Games and Economic Behavior." For von Neumann, game theory was a side project from his main occupation---in 1943 he was consulting on the Manhattan Project to develop the atomic bomb, and from 1944 he worked on designing the first electronic computer. Yet, their joint contribution started a rich research program culminating in the work of John F. Nash, Jr. who initiated the game theoretic study of bargaining. Nash received the Nobel Prize in 1994, along with two other game theorists, John C. Harsanyi and Reinhard Selten. Since then, many other game theorists have been recognized by the Swedish Academy, including, Roger Meryson, Rober Aumann, Amartya Sen, Eleanor Ostrom, and most recently, Jean Tirole.

The course will be centered around several applications of game theory to politics: electoral competition, agenda control, lobbying, signaling in legislatures and coalition games.**Prerequisites**: PLSC 43401 taught by Professor Bobby Gulloty

**PPHA 44900 Social Experiments: Design and Generalization**

The pressure in many fields (notably medicine, health research, and education) for evidence-based results has increased the importance of the design and analysis of social investigations. This course will address three broad issues: the design and analysis of social experiments and quasi-experiments; the design and analysis of sample surveys; and how the interrelationships between the two approaches can inform generalization from experiments. There are two parallel streams in the course. First, the course will tackle the issues of generalization from three different perspectives: (i) the classic statistical design of experiments; (ii) the design of experiments and quasi-experiments in the social sciences; (iii) the design and analysis of smaple surveys. Second, using a set of readings on research design in a variety off settings, we will consider how evidence from research is gathered and used. Randomized clinical trials in medicine, tests of interventions in education and manpower planning, and the use of scientific evidence in policy formulation will be among the examples. **Prerequisites**: Requires familiarity with statisticl inference at the level of PPHA311- basic statistical inference and simple linear regression.

**PPHA 30525 Next Generation Data: Sources, Access, Analytics**

For decades, sample surveys have produced the data that provide the basis for decisions of policy makers and decision makers in both public and the private sectors. Traditional surveys are however coming under a dual threat: decreasing response rates and increasing costs. At the same time a wide array of new sources of data is emerging. Although survey researchers and methodologists are actively seeking to adapt to an ever changing social and technological environnment, it is increasingly difficult to maintain the desired relevance, accuracy, and the timeliness of survey-based statistics. At the same time, there are many potentially valuable non-survey data sources, such as federal, state, and local government administrative records, credit card, and store transactions, sensor data, and a wide and growing variety of web-based data, such as social media, price data, etc. This class will discuss the new forms of data that are being collected to conduct social, economic, behavioral, and policy research, while at the same time addressing innovations in traditional methods, such as survey research. Issues of access, quality, ethics/privacy, analysis, and storage will be discussed. A range of policy domains will be addressed, including education, finance, transportation, welfare programs, and health care. We hope to invite guest speakers to present the perspective of data generators, data providers, and data users. This course counts toward Survey Research Certificate. **Prerequisites**: Harris students will have had two statistics courses (PPHA310 and PPHA311). At least one statistics course would be desirable. Some familiarity with data sets online would be an advantage.

**SOCI 20004/30004 Statistical Methods of Research I**

This course provides an introduction to quantitative methods and a foundation for other methods courses in the social sciences. The course considers standard topics: graphical and tabular displays of univariate and bivariate distributions, an introduction to statistical inference, and commonly arising applications such as the t-test, the two-way contingency table, analysis of variance, and regression. However, all statistical ideas are embedded in case studies including a national survey of adult literacy and an experimental test of alternative methods of writing instruction. For each case study, we will consider the issues motivating the research, the key research questions, and reports of findings. We will then re-analyze the data using the techniques described above, and based on the reanalysis, we will critically evaluate the validity of inferences previously drawn. Thus, the course will consider all statistical choices and inferences in the context of the broader logic of inquiry with the aim of strengthening our understanding of that logic as well as the statistical methods.**Prerequisites**: No prior instruction in statistical analysis required.

**SOCI 20005/30005 Statistical Methods of Research II**

This course aims to prepare students to read, critically evaluate, and conduct research that relies on the most common methods of analysis use in sociology and related social sciences. Date of interest include surveys, large-scale experiments, and non-experiments in which the primay interest is focused on describing the association between one or more explanatory variables and an outcome. The outcomes of interest will include continuous outcomes, including retention in school, graduation from high school, employment, and criminal victimization; and count data, including family size, crime events and absenteeism. The most popular methods of analysis for such outcomes include multiple regression and its generalization to discrete outcomes.**Prerequisites**: Applied statistics at the level of multiple regression (e.g., Soc 30004, Statistical Methods of Research 1)

**SOC 20112/30112 Applications of Hierarchical Models in Longitudinal and Multilevel Research **

A number of diverse methodological problems such as correlates of change, analysis of multi-level data, and certain aspects of meta-analysis share a common feature-a hierarchical structure. The hierarchical model offers a promising approach to analyzing data in these situations. This course will survey the methodological literature in this area, and demonstrate how the hierarchical linear model can be applied to a range of problems. **Prerequisites**: Applied Statistics at a level of multiple regression

**MEDC/ISTP 42000 Topics in data analysis in biomedical research: Big Data**

The technological advances in biomedical research that allow high-throughput methods mean vast data sets are rapidly becoing the norm in the field. This course is intended to introduce medical students in their M4 year to the challenges and opportunities of big data in clinical and translational contexts.

Topics will include: the extent of natural genetic variability in genetics; human genetics and the association with disease; non-genetics databases (such as Medicare) and how they are used fo research; mapping complex disease loci using large datasets; causal inference; the future of bioinformatics.**Prerequisites**: Some calculus and probability theory

**STAT 24400-1 Statistical Theory and Methods I**

This is the first quarter o a two-quarter sequence. Enrollment in the first quarter alone is permitted, although not recommended. The first quarter will cover the basic tools from probability and the elements of statistical theory. Topics will include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distribution, joint probability distributions, and the transformation of random variables, principels of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, lilkelihood ratio tests, multinomial distributions and chi-square tests. Some large sample theory will be included. The emphasis will be upon statistical theory, specifically methodology. **Prerequisites**: Math 19520 or 20000 with a grade of B or better or Math 16300, 20250, 20300, 20700 or STAT 24300 or PHYS 22100. Concurrent or prior linear algebra (MATH 19620, 20250 or STAT 23400 or equivalent) is recommended for students continuing to STAT 24500.

**STAT 26700/36700 History of Statistics**

This** **course covers topics in the history of statistics, from the eleventh century to the middle of the twentieth century. We focus on the period from 1650 to 1950, with an emphasis on the mathematical developments in the theory of probability and how they came to be used in the sciences. Our goals are both to quantify uncertainty in observational data and to develop a conceptual framework for scientific theories. This course includes broad views of the development of the subject closer looks at specific people and investigations, including reanalyses of historical data.**Prerequisites**: Required that students take a course in statistics, preferably one that has taught statistical methods such as the t-test and the chi-square test.

Note(s) Only able to admit about half those who apply, preference being given to 4th year students and graduate students in specific disciplinary masters or phd programs.

**STAT 33910/33170 Financial Statistics: Time Series, Forecastig, Mean Reversion, and high Frequency Data**

This course is an introduction to the econometric analysis of high-frequency financial data. This is where the stochastic models of quantitative finance meet the reality of how the process really evolves. The course is focused on the statistical theory of how to connct the two, but there will also be some data analysis. With some additional statistical background (which cna be acquired after the course), the participants will be able to read articles in the area. The statistical theory is longitudinal, and it thus complements cross-sectional calibration methods (implied volatility, etc.). The course also discusses volatility clustering and market microstructure.

**SOCI 30125 Rational Foundations of Social Theory**

This course is concerned with the introduction to the rational foundation of sociological theory, and covers the following topics: (1) the conceptualizations of social mechanism by Peter Hedstrom and Richard Swedberg, (2) social exchange theory by Peter Blau and by George C. Hoans (3) theory of network exchange and dependence by Robert Emerson and Karen Cook, and by Kazuo Yamaguchi, (4) theory and model of collective action by James S. Coleman, by Mark Granovettor, and by Thomas Schelling, (5) theory/model of relative deprivation by Raymond Boudon and by Kazuo Yamaguchi, (6) social capital theory by James S. Coleman, by Robert Putnum, and by Robert Ffrank, (7) rational theory of emotions by Robert Frank, (8) rational choice theory/model of the family by Stephen Coate and Glenn Loury, (10) rational characterizations of concepts related to Robert K. Merton's theory by Kazuo Yamaguchi, including self-fulfilling prophecy and anticipatory socialization, (11) theories of relatie status by Guillermina Jasso and by Robert Frank and Cass Sunstein, and (12) rational choice theories/models of trust and cooperation especially about prisoner's dilemna situations and social network.**Prerequisites**: No prerequisites. Students are encouraged to take Math Camp (Social Sciences 30100) taught by Mark Hansen in September.

**SOCI 30157 Mathematical Models**

Mathematical models in sociology include (1) models of social processes, (2) models of social relations, and (3) models of social choice. The probability theory is especially relevant for models of social processes, matrix algebra is especially relevant for models of social relations, and multivariate differential calculus is especially relevant for models of social choices.

More substantively, the course intends to cover some aspects of the following topics. (1) processes of social influence and social contagion, (2) social network, (3) balance theory, (4) exchange and power, (5) trust, and (6) collective action.

The major mathematical tools employed in mathematical sociology are (1) probability and stochastic processes, (2) matrix algebra, and (3) multivariate differential calculus. Cognizant of the fact that many students do not know them well, the course covers many reviews of them with simple applications. You will see in the first two handouts a review of probability theory and matrix algebra. There will be more such reviews of probability theory and matrix algebra in later sessions as well as reviews of multivariate differential calculus.**Prerequisites**: No prerequisites. Students are encourage to take Math Camp (Social Sciences 30100) taught by Mark Hansen in September.

**CHDV 30102 Introduction to Causal Inference**

The course** **is designed for graduate students and advanced undergraduate students from the social sciences, education, public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. The goal of this course is to equip students with basic knowledge of and analytic skills in causal inference. Topics for the couurse will include the potential outcomes framework for causal inference; experimental and observational studies; indentification assumptions for causal parameters; potential pitfalls of using ANVOCA to estimate a causal effect; propensity score based methods including matching stratification, inverse-probability-of-treatment-weighting (IPTW), marginal mean weighting through stratification (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RRD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-sectional and panel data, and fixed effects model.**Prerequisites**: Intermediate Statistics or equivalent such as STAT 22400/PBHS 24, PPHA 31301, BUS 41100, or SOC 3005 is a prerequisite. This coures is a pre-requisite for "Advanced Topics in Causal Inference" and "Mediation, moderation, and spillover effects."

**PSYCH 43360 Computational Models of Cognition and Development**

Computational Models are powerful tools for integrating empirial research, and for making novel predictions about cognition and development. This course will survey computational models of attention. Learning, Decision Making, and Language Processing, aiming to develop students' understanding of what models ae for broadly, as well as what kinds of modesl are used and useful in their individual research areas. **Prerequisites**: None

**STAT 24500 Statistics Theory and Methods II**

This course is the second of a two-quarter introduction to the principles and techniques of statistics: the first quarter covers tools from probability and the elements of statistical theory, while the second quarter focuses on statistical methodology, including the analysis of variance, regression, correlation, and some multivariate analysis. Some principles of data analysis are introduced, and an attempt is made to present the analysis of variance and regression in a unified framework. Although theoretical concepts will be discussed, computers will also be used to practice their application. Most of the material covered in this course will be from but not be limited to Chapters 6, 8, 19, 12, 13, and 14 of Rice.**Prerequisite**: Linear algebra (MATH 19620 or 20250 or STAT 24300 or equivalent) and STAT 24400 or STAT 24410.

**STAT 34700 Generalized Linear Models**

This applied course covers factors, variates, contrasts, and interactions; exponential-family models (i.e. variance function); definition of a generalized linear modeal (i.e. link functions); specific examples of GLMS; logistic and probit regression; cumulative logistic modesl; log-linera models and contingency tables; inverse linear models; Quasi-likelihood and least squares; estimating functions; and partially linear models. **Prerequisite**: STAT 34300 or consent of instructor

**ECON 31000 Empirical Analysis I**

This course provides a rigorous introduction to some basic methods in econometrics, including the ordinary least squares (OLS) estimator, (linear) instrumental variable (IV) methods, and maximum likelihood (ML) methods. We begin in the first part of the course by developing the requisite large-sample (asymptotic) theory. Along the way, we will illustrate the use of the asymptotic theory for estimation and inference. In the second part of the course, we will introduce the linear regression model and study the OLS estimator and IV methods for this model. A distinguishing feature of this part of course is an emphasis on different interpretations of the linear regression model, i.e., descriptive versus structural (causal) uses of linear regression. In the final part of the course, we will study ML estimators. We will apply the theory to some limited dependent variable models.**Prerequisites**: Students are expected to be familiar with basic probability theory (the concept of a random variable, expectations, etc.) and statistics. An exposure to linear algebra will also be helpful as well as the mathematical maturity that comes from, say a course in real analysis.

**ECON 31200 Empirical Analysis III**

The course will review some of the classical methods you were introduced to in previous quarters and give examples of their use in applied microeconomic research. Our focus will be on exploring and understanding data sets, evaluating predictions of economic models, and identifying and estimating the parameters of economic models. The methods we will build on include regression techniques, maximum likelihood, method of moments estimators, as well as some non-parametric methods. Lectures and homework assignments will seek to build proficiency in the correct application of these methods to economic research questions.**Prerequisites**: ECON 31100 Empirical Analysis II

**ECON 31703 Topics in Econometrics**

The past two decades have seen quick development of techniques to deal with high-dimensional models and data sets. Some of the main developments come from outside of economics, in fields such as statistics, machine learning and computer science. This course is an introduction to these techniques, with a focus on how to use them in economic applications. **Prerequisites**: Students should be familiar with basic probability and statistics.

**PPHA 31300 Regression Analysis for Public Policy II**

This class covers basic Gauss-Markov theory and other topics in regression analysis.**Prerequisites**: PPHA 31000, PPHA 31200 or equivalent.

**PPHA 41300 Cost Benefit of Analysis**

The goals of this course include learning (1) how to conduct the basic steps of cost-benefit analysis (CBA); (2) how to incorporate elements of cost-benefits analysis into policy work; and (3) when CBA is a good tool to use and when it isn't. This class also presents an opportunity to reflect on "big picture" issues of how to value lives saved; and other difficult matters.**Prerequisites**: Two quarter core microeconomics sequence at Harris or its equivalent, and restricted to second-year students.

**PPHA 42100 Applied Econometrics II**

The goal of this course is for students to learn a set of statistical tools and research designs that are useful in conducting high-quality empirical research on topics applied microeconomics and related fields. Since most applied economic research examines questions with direct policy implications this course will focus on methods for estimating causal effects. This course differs from many other econometrics courses in that it is oriented toward applied practitioners rather than future econometricians. It therefore emphasizes research design (relative to statistical technique) and applications (relative to theoretical proofs), though it covers some of each.**Prerequisites**: PPHA42000 (Applied Econometrics I) is the prerequisite for this course. Students should be familiar with graduate school level probability and statistics, matrix algebra, and the classical linear regression model at the level of PPHA420. In the Economics department, the equivalent of preparation would be the 1st year Ph.D. econometrics cousework.

Per recommendation, do not take this course if you have not taken PPHA420 or a Ph.D. level economterics coursework.

This course is a core course for Ph.D. students and MACRM students at Harris School. Those who are not in the Harris Ph.D. program, the MACRM program, or the economics Ph.D. program need permission from the instructor to take the course.**Please Note: **Harris students are prioritized over non-Harris students due to limited seated.

**PLSC 30700 Linear Models**

The purpose of this course is to bring graduate students up to speed with the dominant method in statistical social scientific research, namely, regression analysis. Regression analysis so ubiquitous in political science (and other social science) research that one will see some variation on the method in just about any volume of the top journals. There are variants of the general idea, of course, but the notion of trying to abstract the "effect" of a change in one variable upon some outcome is the cornerstone of statistical work in our field. **Prerequisites**: SOSC 30100 or MACSS 33000.

**37907-50 Behavioral Science Research Methods in Marketing**

This course will focus on both the philosophical and practical questions involved in conducting behavioral research for academic publication. We will discuss specific research methodologies, best practices and current controversies in research methods. The course assumes prior training in statistics, and the goal of the course is to bridge the gap between formal statistics and day-to-day research practice. The course will provide trianing in commonly used methods as well as develop your intuition for the logic underlying statistical practice, and when the logic is being violated.

Prerequisites: PhD students only. Non-Booth research master's students require instructor permission.

**CAPP 30524 Machine Learning for Public Policy**

This course will be an introduction to machine learning techniques and how to use them to help solve public policy problems. This course is designed for public policy and social science students who are intersted in learning modern, scalable, computational data analysis methods (buzzwords include machine learning, data science, big data, AI), and apply them to social and policy probems.**Prerequisites**: Two courses in Computer Programming (Python experience required), Two courses in Probability & Statistics, Discrete Math and Linear Algebra, Prior experience with data analysis is highly recommended (using SQL, R, Python), familiarity with linux and command line tools, familiarity with git and github.