Applied Econometrics
  This is an intermediate level, Ph.D. course in Applied Econometrics. Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression framework. After a review of the linear model, we will develop the asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods. Inference techniques used in the linear regression framework such as t and F tests will be extended to include Wald, Lagrange multiplier and likelihood ratio and tests for nonnested hypotheses such as the Hausman specification test and Davidson and MacKinnon's J test. Specific modelling frameworks will include the linear regression model and extensions to models for panel data, multiple equation models, and models for discrete choice.
Econometric Analysis of Panel Data
  This is an intermediate level, Ph.D. course in the area of Applied Econometrics dealing with Panel Data. The range of topics covered in the course will span a large part of econometrics generally, though we are particularly interested in those techniques as they are adapted to the analysis of 'panel' or 'longitudinal' data sets. Topics to be studied include specification, estimation, and inference in the context of models that include individual (firm, person, etc.) effects. We will begin with a development of the standard linear regression model, then extend it to panel data settings involving 'fixed' and 'random' effects. The asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models will be reviewed or developed as we proceed.. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods. The linear model will be extended to dynamic models and recently developed GMM and instrumental variables. The classical methods of maximum likelihood and GMM and Bayesian methods, expecially MCMC techniques are applied to the models with individual effects. The second half of the course will focus on nonlinear models. Theoretical developments will focus on heterogeneity in models such as random parameter variation, and on techniques for optimization in the setting of nonlinear models. We will also consider numerous applications from the literature, including static and dynamic regression models, heterogeneous parameters models (e.g., Fama-Macbeth), random parameter variation, and specific nonlinear models such as binary and multinomial choice and models for count data.

Topics in Applied Econometrics

This (occasional) course is a continuation of the econometric theory course. Topics covered focus on microeconometric methods, including binary and discrete choice modeling, efficiency measurement, limited dependent variables, sample selection. Special emphasis is given to estimation methods including maximum likelihood and generalized methods of moments and systems methods of estimation such as structural modeling, simultaneous equations and seemingly unrelated regression systems. A large part of the semester will be spent on recent developments in modeling with panel data. We also examine Bayesian vs. Classical estimation methods, and study simulation based estimation such as maximum simulated likelihood, Gibbs sampling, and hierarchical Bayes estimation. (Topics are determined partly by the preferences of the participants.)

Statistics and Data Analysis

This course will provide students with an understanding of fundamental notions of data presentation and analysis. We will develop tools to enable students to use statistical thinking in the context of business problems. The course deals with modern methods of data exploration (partly to reveal unusual or problematic aspects of data sets), the uses and abuses of the basic techniques of statistical inference, and the use of linear regression as a tool for management and financial analysis.

Regression and Forecasting Models

This undergraduate course will introduce students to the idea of correlation and to the linear regression model. Linear regression is the fundamental statistical technique used in business, economics and all social sciences for understanding relationships among variables and for forecasting observed outcomes. We will develop the simple linear regression of a dependent variable on a single independent variable, then explore the many different forms and uses of the multiple linear regression model. At the end of the discussion, we will develop an extension of the linear regression that is used for modeling and predicting a binary outcome such as whether a consumer purchases a product (or not).

Statistical Inference and Regression Analysis

This course has two parts. In the first we will develop topics in mathematical statistics including distribution theory, estimation theory for the method of moments and maximum likelihood estimation, limit theorems including laws of large numbers and central limit theorems, and principles of statistical inference including confidence intervals and hypothesis testing. The second half of the course will examine linear, loglinear and nonlinear regression models. The course is intended to provide background for research and practice in statistical and actuarial science. Emphasis is on theoretical foundations for model building and statistical analysis.

Understanding Firms and Markets

This course will employ the marginal analysis and the consumers-firms-markets perspectives of microeconomics to enhance MBA students' understanding of the business environments in which they will be working and the important strategic issues -- especially pricing and product choice -- that arise in those environments. Strategy will be a recurring theme. The course structure assumes that all students have had some economics background. They must be comfortable with quantitative concepts and approaches and with graphical/geometric ways of presenting quantitative information. There are a number of important themes/concepts that will pervade the course: Marginal analysis, incentives, and opportunity cost (all as derivatives of the maximizing process), and elasticities as a measuring device; Strategic thinking (e.g., look forward and reason back; search for dominant and dominated strategies); The concept of equilibrium; The presence or absence of market power, and its consequences; The presence or absence of information, and its consequences

Entertainment and Media: Markets and Economics

This course is a survey of economic issues in the entertainment and media industries.  It examines some of the special aspects of these businesses that complicate the market processes, such as the particular nature of demand for experience goods (interdependent preferences, fads), pricing strategy for providers of experience goods, scale economies and vertical integration in production, and obstacles to market equilibrium that motivate public policy.  Industries examined will include:
·    The movie business – the staged project nature of production, vertical integration, peculiar contracting mechanisms and the reasons that nearly all films ‘lose’ money
·    Music and publishing with an emphasis on intellectual property, both legal and economic issues such as valuation and royalties, the implications of new digital media;
·    Television and radio, and the fundamental differences between public and private broadcast markets;
·    Major league sports, and the implications of simultaneous production and consumption, labor markets, and value creation in the sports leagues;
·    Art markets, the creation of and pursuit of economic rents through space and time;
·    Gambling, uncertainty, and certainties of the casino business.