Certificate Requirements and Courses
Choose four of the following nine courses:
This course focuses on the practical uses of time-series econometrics in a macroeconomic context. The topics covered include autoregressive-moving average processes, non-stationary time series models, unit root tests, vector autoregression models, and cointegration analysis. Prerequisites: 440.602 Macroeconomic Theory and Policy; 440.606 Econometrics.
This course examines econometric approaches to forecasting macroeconomic activity. The approaches covered span single equation time series to large, complex, simultaneous equations systems. Different measures to assess the forecasting accuracy of these approaches are addressed. A discussion of these approaches and their relevance for policy recommendations is also covered. Prerequisites: 440.602 Macroeconomic Theory and Policy; 440.606 Econometrics.
The main goal of this course is to provide the students the alternative viewpoint of the Bayesian approach vis-à-vis the classical econometric approach based on the frequentist perspective. The course will present the basic principles of Bayesian inference, Bayesian Analysis of the linear regression model and extensions of the regression model, and the numerical methods used for Bayesian implementation. Modern Bayesian econometrics relies heavily on numerical simulation methods and computational algorithms. With the advancement of computing power and the advent of new simulation methods, simulation based Bayesian methods have become increasingly popular in practice with a large and growing number of applications. A significant part of the course will be devoted to explaining and demonstrating how numerical Bayesian methods, particularly, Markov Chain Monte Carlo (MCMC) methods, such as the Gibbs sampling and the Metropolis-Hastings algorithm, can be applied to estimate various interesting models in economics and finance. Students will develop practical experience with posterior simulation through hands on computer exercises involving computer programming. Prerequisites: 440.601 Microeconomic Theory, 440.606 Econometrics.
[formerly 440.647] This course introduces students to the methods most commonly used in empirical finance. Key models and methods are ARCH, GMM, Regime-Switching Models, test of CAPM (Capital Asset Pricing Model), term structure models, and volatility models (implied, stochastic volatility). Students will also learn aspects of time series econometrics for both stationary and non-stationary variables at different time frequencies, with emphasis on financial variables. Prerequisites: 440.601 Microeconomic Theory and Policy; 440.606 Econometrics; 440.614 Macroeconometrics is recommended..
[formerly 440.648] This course covers a number of advanced techniques frequently encountered in applied microeconometric analysis. Topics include generalized method of moments estimation, nonlinear regression, estimation with panel data, systems of regression equations and simultaneous equation models, maximum likelihood estimation and likelihood ratio tests, and limited dependent variable analysis (i.e. Logit, Probit, Tobit, etc.). Prerequisites: 440.601 Microeconomic Theory and Policy; 440.606 Econometrics.
[formerly 440.632] The objective of this course is to develop and apply an analytical framework for evaluating projects with an emphasis on publicly funded projects. Coverage includes the evaluation of benefits and costs over time, including in the presence of uncertainty, in the absence of market prices, and when income distribution objectives need to be incorporated into a project's evaluation. Prerequisite: 440.601 Microeconomic Theory and Policy. Corequisite: 440.606 Econometrics.
This course will provide an understanding of how to independently develop, modify, run and interpret Computable General Equilibrium (CGE) models. CGE models are widely used in the analysis of International Trade, Taxation, Environmental Policy, and other subjects. The specific objectives of this course are as follows: Students will (1) gain an understanding of the underlying economic theory behind CGE modeling; (2) learn how to gather data sources from publicly available information to build CGE models; (3) gain an understanding of the software General Algebraic Modeling Software (GAMS) to run the models; (4) learn how use and modify existing CGE programs for research purposes; (5) be able to write simple CGE programs in GAMS; (6) be able to analyze public policy with CGE models; (7) how to interpret results from CGE models; (8) understand possible extensions of CGE models for potential future research purposes. Analytical skills developed through this class will assist you in building your careers as researchers, public managers, and policy analysts. Prerequisites: 440.601 Microeconomic Theory, 440.602 Macroeconomics Theory. Corequisite: 440.606 Econometrics.
This course focuses on the use of machine learning methods for in-sample and out-of-sample prediction. The topics include regression, classification, random trees (forests, boosting, and pruning), regularization, Bayesian estimation, neural networks, support vector machines, model selection and ensemble learning. Prerequisite 440.606 Econometrics.
This course introduces students to the theory and practice of conducting surveys. Survey methods combines both social science—economics, sociology, and psychology—and quantitative methods—mathematics, statistics, and computer science—to develop a theory of how surveys can best be used to measure important aspects of the human condition. Key topics include sample design, weighting, data collection modes, administrative operations, questionnaire design, nonresponse, and estimation in surveys. Prerequisites: 440.601 Microeconomic Theory, 440.605 Statistics. Corequisite: 440.606 Econometrics.