Courses

Required Courses

MQEA 501 - Micro Theory, with Implications and Applications

This course establishes core theoretical foundations for a variety of other courses in the program, including Macro Theory, Economic Policy, and Econometrics. Consumer theory, the Theory of the Firm, Production economics, Competitive markets, Monopoly, and Equilibrium, and others, are all potential topics in this course. The course includes an introduction to applied multi-equation models (MEM) as a natural but applied extension of theoretical models. The MEM curriculum, a bridge from theory to application, provides a natural setting for the study of causality, including the direction of causality, the implications of causality, and flowchart representations.

MQEA 502 - Macro Theory, Measurement & Empirics

This course builds on Econ 501 to apply theoretical analysis to the Macroeconomy, and it adds empirical analysis. We start with a brief review of the history of Macroeconomic theory, and we study the historical Keynesian model along with its policy implications. Then the modern Macro-model is introduced and an in-depth treatment of its implications for a modern and dynamic economic analysis are covered, including: transitions versus steady-states, anticipated versus unanticipated impacts, temporary versus permanent impacts, and more. This modern Macroeconomic model is used to provide insights to economic growth and an in-depth analysis of Fiscal policy implications. This course concludes with an introduction to economic measurement and empirics.

MQEA 503 - Economic Policy Analysis

The course provides insight into the complex workings of the economy from both a theoretical and empirical perspective. Microeconomic and Macroeconomic theory are used as a foundation for policy analysis and empirical application. The course will be divided into two overlapping sections – (1) Principles of Economics and (2) Policy Analysis. The policy analysis will utilize the tools provided by economic principles and will then combine these with empirical analysis. In addition to completing problem sets and quizzes, each student will be responsible for two policy briefs which involve writing and presenting economic analysis.

MQEA 525 - Statistics, the Classical Model, Identification & Causal Inference

This course covers the fundamentals of applied econometrics. The first section will consist of probability and statistics which provide the theoretical framework within which we will conduct regression analysis. The second section will include a thorough review of the Classical Regression model, identification, and causal inference. Some instruction will occur in the computer lab, where students will estimate regressions using actual data and interpret the results. Problem sets and a practical regression-based project will be assigned to each student.

MQEA 526 - Advanced Techniques & Extensions of the Classical Model

This course covers extensions of the Classical Regression model. First, we will consider problems that arise with the classic linear model, including heteroskedastic errors and endogenous variables. Second, we will thoroughly study important extensions to the classical model, including Instrumental Variables estimation techniques, non-linear models, large-sample distribution theory, Maximum Likelihood Estimation, and estimation techniques for binary response data. Problem sets and a practical regression-based project will be assigned to each student.

MQEA 527 - Time-Series Analysis, Inertial Forecasting & Cointegration Modeling

This course covers time-series methods. We will begin with serial correlation as a violation of the assumption of the Classical Model. We study Time-Series Analysis, including univariate methods, topics will include white noise, random walks, stationarity, seasonality, and ARIMA modeling. Next we study deterministic trends, stochastic trends, and a discussion of business cycles in the context of time-series econometrics. Next we will test for trends and unit roots and study Intervention models. The later part of the course will cover Vector-autoregressions (VAR), and Cointegration techniques (SE-ECM and FM-OLS). Students complete their homework assignments using computer programming. The class will include “Mini-Project” assignments, where the student will write a professional-style report.

MQEA 528 - Causal Multi-Equation Modeling, Scenario Forecasting & Error Analysis

This course turns its attention to causal and non-causal multi-equation modeling and forecasting frameworks. We will use computer programming to build such models and use them to generate forecasts. We study how causality and data structures provide guidance for the structure of causal models. Additional topics include but are not limited to: consensus forecasting, subjective forecasting, intervention modeling, scenario forecasting, the Lucas Critique, in-sample and out-of-sample methods, forecast-error measurement, and combination forecasting. The Capstone modeling project consists of a multi-frequency, multi-platform, and multiple-model database and forecast system that creates various forecasts, conducts forecast-error analysis, and builds combination forecasts. The Capstone includes PowerPoint presentations and students build a professional-style written report.

MQEA 535 - Financial Economics (Theory & Application of Financial Market Value)

This course includes an overview of financial markets and instruments, time value of money, term structure of interest rates, modern portfolio theory, efficient market hypothesis, mechanics of derivatives markets, valuation and hedging principles, and risk management applications. The class will do a deep dive in the financial structure of the firm and will cover the Balance Sheet, Profit and Loss Statement and the Statement of Cash Flows. Included in this class is a detailed financial forecast of a firm; the forecast design will bring all the class concepts into play. The text adds value to the estimates and explains the nuances of a robust financial forecast. The instructor will provide the forecasting Excel spreadsheet on Canvas. The forecasting spreadsheet will require the use of regression analysis.

MQEA 551 - Foundations of Analytics and Data Science

As organizations look for ways to leverage data to create value, analytics has become an important source of competitive advantage for businesses. This course provides a hands-on introduction to the collections of predictive modeling techniques used to extract patterns and trends from data, enabling informed business decisions. The topics covered include data preparation, data visualization, predictive analytics, and decision- making under uncertainty. The course includes hands-on work with data and the SAS JMP Pro statistical software package. By the end of the course, you will be able to identify opportunities for creating value using predictive modeling techniques, employ the techniques to derive results, interpret the results and comprehend the limitations of the results.

MQEA 552 - Advanced Machine Learning and Predictive Analytics

The course focuses on the application of machine learning methods explored in Analytics I, which use data and statistical techniques to predict outcomes. Students will learn through a hands-on approach to build and tune models using R to predict categorical and continuous outcomes, test those models, interpret and present the results. The focus will be on applying advanced machine learning models implemented in R while balancing the trade-off between prediction power and model interpretability. The course covers how to formulate a model for a given decision problem, perform analysis to generate insights, and effectively communicate those insights.

Foundations Course

MQEA 500 - Mathematics for Economists

Mathematics for Economists will cover a variety of mathematical and statistical topics that are used in core MSQE curriculum. These would include: logarithmic/exponential/ polynomial functions, differential calculus, integral calculus, the Taylor approximation, static optimization, the Lagrangian technique, matrix algebra, basic linear algebra and systems of linear equations, probability, probability distributions, the normal distribution, moments, statistical estimation, the Central Limit Theorem, hypothesis testing, simple linear regression, and the matrix form of the multiple linear regression model.