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1 - 10 of 16 results for: MGTECON

MGTECON 300: Growth and Stabilization in the Global Economy

This course gives students the background they need to understand the broad movements in the global economy. Key topics include long-run economic growth, technological change, wage inequality, international trade, interest rates, inflation, exchange rates, and monetary policy. By the end of the course, students should be able to read and understand the discussions of economic issues in The Economist, the Wall Street Journal, the New York Times, or the Congressional Budget Office.
Units: 3 | Grading: GSB Letter Graded

MGTECON 327: Business and Public Policy Perspectives on U.S. Inequality

This class will analyze the growth in inequality in the US over the last several decades and how that trend is likely to continue or change in the future. We will ask if and how public policy can affect inequality. We will also focus on business's role -- what are the responsibilities of private sector companies, how does inequality affect them, and how should the growth in inequality affect their strategies? We will look at inequality in income, some of its potential sources, and its effects in other areas. Specifically, we will look at education, housing, the social safety net, migration, and the job market. The class will be very interactive and will be based on readings drawn from academic research, case studies, news, and opinion readings. We will also have guest speakers from industry, government, and non-profits. The class will be co-taught by a GSB labor economist and an advisor to policy makers with decades of business experience.
Units: 3 | Grading: GSB Letter Graded

MGTECON 515: Cryptocurrency

This class will provide an overview of the rapidly evolving area of distributed ledger and blockchain technologies, with a focus on economic and strategic issues. We will cover key components of the architecture that affect the products derived from cryptocurrency. We then consider tokens as a store of value and exchange, analyzing models of cryptocurrency pricing and as a vehicle for raising of capital. Next, we consider use cases including payments, micropayments, asset registries, and smart contracts. We then analyze barriers to entry in cryptocurrencies, as well as how the new products they enable affect industry structure in both the financial sector and the economy and society as a whole. For example, how might decentralized systems like the blockchain impact the sharing economy? The government? We consider the governance of these decentralized systems and how decentralization affects the potential for the management and success of platforms. We discuss the potential for national digital currencies and the end of cash. Finally, we consider consumer protection, privacy, security, regulation, and the power of governments and regulators over borderless, decentralized systems. Students will benefit from guest lectures by industry and thought leaders.
Units: 2 | Grading: GSB Letter Graded

MGTECON 533: Economics of Strategy and Organization

The goal of this class is to combine economic theory and business practice to develop insights for understanding and creating business strategy and organizations. We will discuss strategies and organizations of companies, identify potential problems and explore potential solutions. Some of the topics to be covered will be how economics of scope and scale can help or undermine business strategy. Economics of vertical and horizontal integration, foreclosure, innovation management. The course will be based on a mixture of formal and informal cases as well as readings describing the underlying economics used in our analysis.
Units: 2 | Grading: GSB Letter Graded

MGTECON 602: Auctions, Bargaining, and Pricing

This course covers mostly auction theory, bargaining theory and related parts of the literature on pricing. Key classic papers covered in the course are Myerson and Satterthwaite on dynamic bargaining, Myerson on optimal auctions, and Milgrom and Weber's classic work, the Coase Conjecture results. We also cover a few more recent developments related to these topics, including dynamic signaling and screening. In some years we also cover topics in matching theory.
Units: 4 | Grading: GSB Student Option LTR/PF

MGTECON 605: Econometric Methods III

This course completes the first-year sequence in econometrics. It develops nonparametric, semiparametric and nonlinear parametric models in detail, as well as optimization methods used to estimate nonlinear models. The instructor will discuss identification issues, the statistical properties of these estimators, and how they are used in practice. Depending on student and instructor interest, we will consider advanced topics and applications, including: machine learning, simulation methods and Bayesian estimators.
Units: 3 | Grading: GSB Letter Graded
Instructors: Reiss, P. (PI)

MGTECON 608: Multiperson Decision Theory

Students and faculty review and present recent research papers on basic theories and economic applications of decision theory, game theory and mechanism design. Applications include market design and analyses of incentives and strategic behavior in markets, and selected topics such as auctions, bargaining, contracting, signaling, and computation.
Units: 3 | Grading: GSB Pass/Fail
Instructors: Wilson, R. (PI)

MGTECON 628: Reading Group in Industrial Organization

This course meets weekly on Tuesdays at Noon. The primary purpose of the course is to read and discuss current working papers in Industrial Organization and related fields (e.g., Econometrics, Marketing, and Labor). Students are required to present papers a couple of times per quarter and both students and faculty may also present their own working papers.
Units: 1 | Repeatable for credit | Grading: GSB Student Option LTR/PF
Instructors: Benkard, L. (PI)

MGTECON 632: Topics in Continuous Time Dynamics

This seminar-style course studies a selection of micro-economic models in dynamic settings, and explores the use of continuous-time methods to solve them. Topics to be covered include experimentation games, social learning, principal-agent problems, career concerns/market-agent models, security design and strategic trading. For every topic discussed, the class introduces gradually the set of relevant mathematical tools: dynamic programming and Hamilton-Jacobi-Bellman equations, Pontryagin's maximum principle, Euler-Lagrange equations, Brownian and Poisson processes, Bayesian inference and linear filtering, change of measure, martingale representation, Malliavin derivatives, stochastic maximum principle, expansions of filtrations. The course emphasizes high-level intuition rather than mathematical rigor. It is targeted at those who seek to become familiar with the literature on continuous-time dynamics and want to understand the functioning of these models, either by general interest or to apply these techniques.
Units: 3 | Grading: GSB Student Option LTR/PF
Instructors: Lambert, N. (PI)

MGTECON 634: Machine Learning and Causal Inference

This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider causal inference methods based on randomized experiments as well as observational studies, including methods such as instrumental variables and those based on longitudinal data. We consider the estimation of average treatment effects as well as personalized policies. Lectures will focus on theoretical developments, while classwork will consist primarily of empirical applications of the methods. Prerequisites: graduate level coursework in at least one of statistics, econometrics, or machine learning. Students without prior exposure to causal inference will need to do additional background reading in the early weeks of the course.
Units: 3 | Grading: GSB Student Option LTR/PF
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