11 - 20 of 41 results for: MGTECON

Poverty rates have fallen markedly in countries around the world, as more households have joined the lower middle-class. Indeed, though U.S. income inequality has increased, inequality has fallen around the world. However, by developed country standards, poverty remains pervasive. What has caused the decline in rates of poverty and can we expect further decreases or can we act to accelerate the improvements? One answer is that countries that have experienced 'inclusive growth'?, in which the growth of the economy (i.e., GDP) has elevated the incomes of the poor, have done better at creating jobs for the poor, especially in the private sector. Therefore, the class will consider the evidence on the factors that have contributed to inclusive economic growth in developing countries. A second answer as to why poverty has fallen, but remains at high levels, is that governments and aid agencies and foundations have targeted programs to the poor. This course discusses macroeconomic policy, targeted government policies, aid, and entrepreneurship in developing countries. Examples will be given from Latin America, South Asia, and Africa. The course is co-taught by a Stanford economist and a World Bank consultant and will build on examples from recent experiences. The class is aimed at GSB students who are either intellectually curious about the topic or anticipate doing business in developing countries.

The goal of this class is to use economic thinking and business practice to develop insights for business strategy and organization design. We will discuss strategies and organizations of companies, identify potential problems, and explore potential solutions. Some of the topics to be covered will be why many established companies find it hard to innovate (and what strategies can mitigate those problems), business-model innovation, and what economic and practical problems arise when companies need to stop projects. The course will be based on a mixture of formal and informal cases. The focus will be on discussing cases related to business strategy and not on developing new economic models.

In this course we will discuss statistical and econometric methods for assisting in decision making. We will discuss experimental designs, ranging from classical A/B tests to modern designs in use at leading tech companies. We will also discuss methods for observational studies, and how one can ensure that the results based on such studies lead to credible decision making. The methods will draw on the econometric literature as well as the modern machine learning literature. Students will be asked to analyze data and prepare reports and presentations based on them.

Businesses are increasingly expected to have a positive social impact, as evidenced by the rapid growth of impact investing. Yet, even as the impact investing market has taken off, impact measurement has lagged, undermining the credibility of the sector. Impact measurement is also critical to individual firms, not-for-profits and governments, as they face increased pressure to generate quantifiable results. How can we measure impact? What are some of the most effective frameworks, tools and approaches for impact measurement? How does one choose the most appropriate measurement tool based on factors such as the size, maturity and sector of an organization? Can impact be distilled down to a single monetary measure, such as a dollar? These and other questions will be explored in considerable depth, primarily through analysis and discussion of case studies. This course is a good match for students interested in impact measurement, impact investing, profit-with-purpose businesses or the role of business in society. The course will be taught by Matt Bannick, who led the impact investing firm, Omidyar Network, and served as the President of PayPal and of eBay International.

This course provides an introduction to the foundations of modern microeconomic theory. Topics include choice theory, with and without uncertainty, consumer and producer theory, dynamic choice and dynamic programming, social choice and efficiency, and fundamentals of general equilibrium.

This course studies the roles of information, incentives and strategic behavior in markets. The rudiments of game theory are developed and applied to selected topics regarding auctions, bargaining, and firms' competitive strategies; information economics; and contracting and market design.

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.

This is the first course in the sequence in graduate econometrics. The course covers some of the probabilistic and statistical underpinnings of econometrics, and explores the large-sample properties of maximum likelihood estimators. You are assumed to have introductory probability and statistics and matrix theory, and to have exposure to basic real analysis. Topics covered in the course include random variables, distribution functions, functions of random variables, expectations, conditional probabilities and Bayes' law, convergence and limit laws, hypothesis testing, estimation (including maximum likelihood estimation, method of moments and Bayes estimation), confidence intervals, and regression analysis

Second course in the PhD sequence in econometrics at the Economics Department (as Econ 271) and at the GSB (as MGTECON 604). This course presents modern econometric methods with a focus on panel regression, machine learning, and time series. Among the topics covered are: estimation and linear regression recap; panel data methods including differences in differences, event studies, fixed-effect models, synthetic control; machine learning methods including supervised and unsupervised learning; uses of machine learning as a tool in econometrics and causal inference; statistical decision theory including econometrics with misaligned preferences; time-series models including state-space models and dynamic stochastic general equilibrium models. Prerequisites: This course assumes working knowledge of basic probability theory, statistics, econometrics, and causal inference as covered in Econ 270 / MGTECON 603.