Applied Economics (AEC)

AEC 504. Fundamentals of Economics. 3 credits

This is a course meant for entering doctoral students with insufficient background in economics. Topics covered include markets and prices, consumer behavior, individual and market demand, choice under uncertainty, production, competitive markets, monopoly and monopsony, competitive strategy, markets with asymmetric information, externalities, and public goods. Offered in the summer, primarily for entering doctoral students.

AEC 505. Real Analysis. 3 credits

The course introduces mathematical tools especially useful in economics, econometrics, and finance. Topics include a basic topology of the real line, sequences and series, limits, continuity, differential and integral calculus. Offered in the summer, primarily for entering doctoral students.

AEC 506. Probability Theory. 3 credits

This course teaches Random Variable, Distribution, Independence; Transformations and Expectations; Common Families of Distributions; Multiple Random Variables, and Markov Chains. Offered in the summer, primarily for entering doctoral students.

AEC 510. Phd Workshop In AEC. 3 credits

The workshop provides a forum for the presentation of ongoing and completed research projects by PhD students in the economics core. Third- and fourth-year PhD students are expected to participate actively.

AEC 511. Advanced Price Theory I. 3 credits

Provides a survey of the substance and methods of contemporary price theory for students preparing to do research. Generally, the course covers the economic behavior of individuals and firms in a competitive market setting. Individual behaviors examined include responses to price and income changes, intertemporal planning (e.g., saving), household production, labor supply, investment in human capital, search, and reactions to uncertainty about future assets and goods prices. For firms, the implications of value-maximization for input demands and output supplies are explored thoroughly. Managerial choices related to multiple products, intertemporal production planning and uncertainty are explicitly modeled. Some extensions to monopoly behavior are considered. Finally, some implications of consumer and competitive firm behavior for industry (single market) and general equilibrium are examined. These include (for industry equilibrium) the technological determinants of industry responses (entry-exit, quantity changes, price changes) to economic shocks such as shifts in demand for the industry's product. For general equilibrium, the first and second welfare theorems will be covered.

AEC 513. Industrial Organization Theory. 3 credits

This course provides an introduction to the theory and practice of industrial organization. Broad areas of application include static oligopoly models, two-stage games and games with infinite horizons. Concepts from game theory such as Nash equilibria, subgame perfect equilibria, and perfect Bayesian equilibria will be used as needed. Special topics may include: contracts, patents, licensing, bundling, tying, buyer-seller networks, switching costs, price discrimination, mergers and entry barriers.

AEC 514. Game Theory. 1 credit

This course teaches the tools of game theory and contract theory, and applies them to topics in industrial organization, organizational economics and other areas. Game theory is the study of strategic interaction among a small number of decision-makers. Nowadays, it is applied in almost any area of economics, as well as in related disciplines such as finance, accounting, marketing, and operations research. Contract theory is concerned with the optimal design of contracts (and at a larger scale, organizations) that define the 'rules of the game' under which agents (such as a firm's employees) interact. In this sense, it can be thought of as an extension of game theory. Contract theory is the methodological basis of much of modern organizational economics, but its methods are applied in many other contexts, too notably, finance. The course is organized by concepts and methods, but most time will be spent on applying them to a large variety of topics.

AEC 520. Causal Inference. 3 credits

The course will cover how to design compelling research, the focus of which is causal inference. The course covers the design of true experiments and concepts of validity (internal validity, external validity, replicability). The approach should follow the Rubin potential outcomes framework. The course then covers causal inference and related econometric methods in observational studies for cross-sectional, panel data, and time-series, and non-linear models including OLS, instrumental variables, Heckman selection models, regression discontinuity designs, matched sample designs, granger causality, event studies, diff-in-diff, fixed effects, clustering standard errors, dynamic panel methods (e.g., Blundell and Bond 1998), and some issues in logit/probit/multinomial logit. Although the course will discuss many econometric techniques, students are expected to have already learned the mechanics of these methods, so that the course can focus on causal inference and its limitations in these methodologies.

AEC 521. Adv Topics In Org Of Industry. 3 credits

The course concentrates on unsettled areas in industrial organization, exposing students to potential thesis and research projects. Specific topics vary from year to year. Typical current topics are theory of conglomerate mergers, analysis of advertising and scale as barriers to entry, quality competition and market responses to costly information.

AEC 523. Micro-Econometric Modeling: Static Approaches. 3 credits

This course introduces students to canonical modeling approaches for analyzing decision making by both firms and consumers, focusing on static environments. Central topics include demand estimation, models of strategic interaction, networks and platforms and auctions. Applications include firm pricing decisions, new product introductions, strategic entry and vertical relationships. The course generally includes coding assignments and student presentations, in addition to the weekly lectures on methods and applications.

AEC 524. Micro-Econometric Modeling: Dynamic Approaches. 3 credits

This course examines consumer and firm behaviors that involve inter-temporal trade-offs and as a result involve dynamic optimization on the part of both consumers and firms. It begins with an overview of dynamic programming methods, in both single and multi-agent settings, emphasizing methods that link estimation with computation. Single agent topics include models of capital replacement, dynamic demand, inventory models and salesforce management. Multi-agent topics include strategic innovation, learning by doing, demand smoothing, and product repositioning. A strong emphasis is placed on recent methods and frontier topics. The course generally includes coding assignments and several student presentations, in addition to weekly lectures.

AEC 525. Intro Math Econ. 4 credits

This course covers the use of optimization theory in economic analysis. The topics covered include finite-dimensional optimization (unconstrained optimization, Lagranges Theorem, the Kuhn-Tucker Theorem), the role of convexity in optimization, parametric continuity of solutions to optimization problems, and finite- and infinite-horizon dynamic programming.