121 - 130 of 158 results for: ECON

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 regression. Among the topics covered are: linear regression and its interpretation, robust inference, asymptotic theory for maximum-likelihood und other extremum estimators, generalized method of moments, Bayesian regression, high-dimensional and non-parametric regression, binary and multinomial discrete choice, resampling methods, linear time-series models, and state-space models. As a prerequisite, this course assumes working knowledge of probability theory and statistics as covered in Econ 270/ MGTECON 603. Enrollment is limited to Econ PhD students for the first two weeks of open enrollment, after which the remaining space will be available to all other interested students. Prerequisites: Econ 270/ MGTECON 603 or equivalent.

Methods for modern causal inference, including identification, matching methods, instrumental variables, regression discontinuity designs, difference in differences, synthetic control methods. Prerequisites: Econ 271 or permission of instructor.

Possible topics: parametric asymptotic theory. M and Z estimators. General large sample results for maximum likelihood; nonlinear least squares; and nonlinear instrumental variables estimators including the generalized method of moments estimator under general conditions. Model selection test. Consistent model selection criteria. Nonnested hypothesis testing. Markov chain Monte Carlo methods. Nonparametric and semiparametric methods. Quantile Regression methods.

(Formerly 273B); Possible topics: nonparametric density estimation and regression analysis; sieve approximation; contiguity; convergence of experiments; cross validation; indirect inference; resampling methods: bootstrap and subsampling; quantile regression; nonstandard asymptotic distribution theory; empirical processes; set identification and inference, large sample efficiency and optimality; multiple hypothesis testing; randomization and permutation tests; inference for dependent data.

This course presents methods for constructing econometric specifications and systems directly based on economic models. One such approach formulates stochastic economic models that give rise to empirically implementable econometric models. The discussion will cover methods for estimating, diagnostic testing, and drawing inferences about the underlying economic primitives, including both parametric and non-parametric identification of economic structures. Applications include models from all fields of empirical microeconomics Industrial Organization, Labor, Public Finance, and Energy and Environmental Economics. Examples include: consumer demand models integrating corner solutions, intertemporal models of household and firm behavior, and dynamic models of single and multi-agent interactions with complete and incomplete information. These include auction markets, oligopolies, regulator-firm interactions, and nonlinear pricing.. The major theme of the course is to present a general framework for economic theory-based empirical research that allows researchers to recover the underlying economic primitives driving observed outcomes of an economic environment. Prerequisites: Econ 202, 203, 204, 270, 271, 272.

This is the first part of a three course sequence (along with Econ 279 & 280-formerly 277) on behavioral and experimental economics. The sequence has two main objectives: 1) examines theories and evidence related to the psychology of economic decision making, 2) Introduces methods of experimental economics, and explores major subject areas (including those not falling within behavioral economics) that have been addressed through laboratory experiments. Focuses on series of experiments that build on one another in an effort to test between competing theoretical frameworks, with the objects of improving the explanatory and predictive performance of standard models, and of providing a foundation for more reliable normative analyses of policy issues. Prerequisites: 204 and 271, or consent of instructor.

This is part of a three course sequence (along with Econ 278 & 280-formerly 277) on behavioral and experimental economics. The sequence has two main objectives: 1) examines theories and evidence related to the psychology of economic decision making, 2) Introduces methods of experimental economics, and explores major subject areas (including those not falling within behavioral economics) that have been addressed through laboratory experiments. Focuses on series of experiments that build on one another in an effort to test between competing theoretical frameworks, with the objects of improving the explanatory and predictive performance of standard models, and of providing a foundation for more reliable normative analyses of policy issues. Prerequisites: 204 and 271, or consent of instructor.

Economics 280 (formerly ECON 277) is a course primarily directed at graduate students in the Economics department writing dissertations with behavioral or experimental components. Economics 280 is the third part of a three course sequence (along with Econ 278 & 279). The first two quarters, which are taught primarily in lecture format, have two main objectives: 1) examining theories and evidence related to the psychology of economic decision making; 2) introducing methods of experimental economics, and exploring major subject areas (including those not falling within behavioral economics) that have been addressed through laboratory experiments. Focuses on series of experiments that build on one another in an effort to test between competing theoretical frameworks, with the objectives of improving the explanatory and predictive performance of standard models, and of providing a foundation for more reliable normative analyses of policy issues. This third quarter is a practicum, focused on students who have taken (at least one of) the first two quarters and who are now preparing an experimental or behavioral study of their own. Prerequisites: Non-Econ Phd students must complete 204 and 271, or have consent of instructor.

This is a team-based course where students will work on a project to design and carry out an experiment intended to drive social impact in collaboration with a partner organization. The first few weeks will include lectures, hands-on tutorials, and labs designed to guide students through the process of experimental design in the digital context. Special topics include designing and selecting outcome measures that capture the impact of interventions; multi-stage experiments with applications to chatbots; learning how treatment effects vary across subgroups; adaptive experiments using bandits and artificial intelligence; and estimation of policies that target treatments based on subject characteristics. Experiments may be conducted on the customer base of a partner organization through their digital applications or on recruited subjects, such as subjects recruited to interactive chatbots. The teaching team will provide templates and technical assistance for designing and running the experiments. Students from different disciplinary backgrounds will be assigned roles to work in teams on the project. This course is part of the GSB's Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will learn research-validated foundations, tools, and practices; apply these tools and learnings to a real project for an external organization; create value for the organization by providing insights and deliverables; and be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to gain practical industry experience and exposure to the organization, its industry, and the space in which it operates; build relationships in the organization and industry; and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages. Prerequisite: Econ 102B or equivalent. Students complete applications and enrollment will be with instructors consent. ECON 281 is for non-GSB students.