Curriculum and Course Descriptions

Program Structure

The Master of Computational Economics (MCECON) at Rice University is structured as a rigorous four-semester (two-year) non-thesis master's program. Students are required to commit to full-time enrollment and must maintain residency throughout all semesters of their study. The curriculum typically involves a consistent course load of three courses per semester. The majority of these classes carry three credits, with the notable exception of SOPE 503, which is a five-credit course, reflecting its intensive nature.

To successfully satisfy the degree requirements, students must complete a minimum of 38 graduate-level credit hours (courses at the 500-level or above). A significant portion of this coursework, at least 32 credit hours, must be completed directly at Rice University to fulfill residency requirements. Academic performance is also a key component, with a minimum overall GPA of 2.67 required for degree conferral. The program also offers a distinctive provision for current Rice undergraduates, allowing for early admission and enrollment into graduate-level courses. This pathway, often referred to as a "fifth-year master's," provides a seamless transition for qualified undergraduates to pursue advanced studies in computational economics.

Core Curriculum Overview

The core curriculum comprises eight required courses, totaling 27 credit hours, ensuring a comprehensive and foundational understanding:

• ECON 501 Microeconomics I (3 credits)
• ECON 510 Econometrics I (3 credits)
• ECON 511 Econometrics II (3 credits)
• SOPE 503 Quantitative Methods for Program Evaluation (5 credits)
• ECON 631 Mathematical Foundations for Computational Economics (3 credits)
• ECON 632 Data Tools for Computational Economics (3 credits)
• ECON 633 Dynamic Models for Computational Economics (3 credits)
• ECON 634 Machine Learning and Algorithms for Computational Economics (3 credits)

Specialization Tracks

After students complete the core curriculum, they are able to tailor their educational journey to specific professional aspirations. The MCECON program offers a choice of four distinct specialization tracks. Within their chosen track, students are required to complete four elective courses from a carefully curated list, ensuring that their education is both comprehensive and practically oriented towards their diverse professional goals.

• Industrial Organization Track: This track focuses on in-depth analysis of market structures, competitive dynamics, pricing strategies, and the impact of regulatory policies. Graduates from this specialization are well-prepared for careers in consulting firms, corporations, government agencies, and economic think tanks.
  
• Applied Microeconomics Track: This specialization trains students in the rigorous assessment of the effectiveness, efficiency, and equity of various policy interventions. This preparation equips them for impactful roles in government agencies, non-governmental organizations (NGOs), and sectors such as education and health, as well as international organizations like the World Bank.
  
• Macroeconomics and Finance Track: This track provides students with the essential skills to analyze large-scale economic trends and policies. Career paths for graduates include positions in government agencies (e.g., the Federal Reserve, Central Banks), international organizations (e.g., the International Monetary Fund), consulting firms, and the broader financial sector.
  
• Academic Track: Develops skills essential for Ph.D. programs in Economics. In this track, the electives will consist of three first-year and one second-year Ph.D. class at Rice Economics. We note that ECON 508 is also a five-credit course, and this track requires 40 credit hours for completion. We will admit into the second year of our Ph.D. program (by waving 501, 502, 504, 505, 508, 510, and 511) any student who satisfies all MCE requirements, including the courses in this field.

Proposed Study Plan (Semester-by-Semester Breakdown)

The MCECON program follows a carefully designed four-semester sequence, ensuring a logical and progressive development of skills, from foundational concepts to advanced applications. This structured plan provides a clear roadmap for students' academic journey, enabling them to anticipate their coursework and manage their time effectively.

Course Descriptions

Core Course Descriptions

• Microeconomics I (ECON 501, 3 credits): This course provides a foundational understanding of how economic agents, including individuals, households, and firms, behave in theoretical contexts.
• Econometrics I (ECON 510, 3 credits): This course establishes the fundamental principles of empirical methods utilized in economics.
• Econometrics II (ECON 511, 3 credits): Building on Econometrics I, this course further develops the basis of empirical methods crucial for economic analysis.
• Quantitative Methods for Program Evaluation (SOPE 503, 5 credits): This core course focuses on the application of quantitative methodologies specifically tailored for program evaluation.
• Tools for Computational Economics (ECON 630): This foundational, online, and self-paced course (0 credits) is designed to be completed over approximately 12 weeks, typically prior to the commencement of the fall semester. It serves as a crucial primer, introducing students to the Python programming language, fundamental software engineering principles, and Git for version control—all essential skills that will be extensively utilized in subsequent courses throughout the program.
• Mathematical Foundations for Computational Economics (ECON 631): Offered in the Fall semester, this 3-credit course provides a comprehensive review of essential mathematical tools, including calculus and linear algebra. It also introduces foundational concepts in random variables, model building, and both frequentist and Bayesian estimation techniques. The course has a dual objective: to empower students to construct models for analyzing real-world phenomena and to cultivate the mathematical maturity necessary for independent learning of new, mathematically sound material. The class combines lectures with hands-on lab time, necessitating that students bring their laptops.
• Data Tools for Computational Economics (ECON 632): This 3-credit course, offered in the Spring semester, equips students with state-of-the-art tools for data manipulation and management using the Python programming language. It heavily utilizes core scientific and data-specific libraries such as NumPy, SciPy, Matplotlib, and Pandas. The course places significant emphasis on handling "real-world" messy datasets, constructing informative visualizations, and implementing basic data engineering best practices.
• Dynamic Computational Economics (ECON 633): A 3-credit course offered in the Spring semester, this course introduces both deterministic and stochastic dynamic processes as powerful tools for addressing questions in the social sciences. It covers key topics such as dynamic programming, time-series analysis (from both Bayesian and frequentist perspectives), Markov models, and Hidden Markov Models. These tools are applied to a variety of current research questions, preparing students for cutting-edge analysis. A solid mastery of the concepts covered in ECON 631 (Mathematical Foundations) and ECON 632 (Data Tools) is a prerequisite.
• Machine Learning and Algorithms for Computational Economics (ECON 634): This 3-credit course, offered in the Fall semester, delves into foundational and frontier machine learning algorithms and models used for analyzing economic and social science data. Its analytical goals encompass data description, reduction, detection of relationships among variables, and the interpretation of these relationships within underlying economic and social forces. The course also teaches how estimated models can be effectively used for prediction, forecasting, and drawing inferences about cause and effect. Topics include supervised learning (regression and classification), reinforcement learning, and model selection via validation procedures, with practical application using high-performance libraries like TensorFlow and PyTorch. Prerequisites include comfort with Python (NumPy, Pandas) and a strong mathematical background in calculus, linear algebra, probability, and statistics.

Elective Course Descriptions

Students have the opportunity to deepen their expertise by choosing four elective courses from a diverse range of offerings. These electives are designed to align with their chosen specialization track, building upon the core curriculum and allowing for in-depth exploration of specific areas of economic inquiry and computational application.

• ECON 502 Macroeconomics (3 credits): This course reviews static general equilibrium theory, elements of functional analysis for optimization, and deterministic and stochastic difference equations with local stability analysis. It introduces Markov processes and dynamic optimization techniques, including stochastic optimal control theory, dynamic programming, and robust control, with applications to growth theory, search, industrial organization, monetary economics, and dynamic stochastic general equilibrium modeling.
• ECON 504 Computational Economics (3 credits): This course covers numerical methods most commonly used in economics and their application to frontier research projects in economic modeling, including optimization theory and numerical integration.
• ECON 505 Financial Economics I (3 credits): An introduction to asset pricing and portfolio choice theory, covering mathematical analysis of single-period and dynamic models, pricing by arbitrage, mean-variance analysis, factor models, dynamic optimization, recursive utility, and an introduction to continuous-time finance.
• ECON 508 Microeconomics II (5 credits): This course consists of two modules: (1) an introduction to the mathematical tools of game theory and the modeling of economic settings as games (normal-form, extensive-form with perfect information, Bayesian, and extensive-form with imperfect information); and (2) an introduction to information economics and mechanism design theory, applying tools from game theory and linear and non-linear methods.
• ECON 512 International Trade (3 credits): This course explores classical, neoclassical, and modern trade theory, including welfare aspects of trade, such as the theory of commercial policy, with a strong emphasis on applications.
• ECON 514 Empirical Industrial Organization I (3 credits): Topics include structural analysis of auctions, nonlinear pricing, insurance, and bargaining data, emphasizing advanced econometric methods (nonparametric and semiparametric) to estimate and test models under incomplete information.
• ECON 515 Labor Economics (3 credits): This course involves the mathematical and statistical analysis of empirical evidence and theories related to various features of labor markets. Topics may include fertility, health, criminal behavior, labor force participation, hours of work, education and training, geographical and inter-firm labor mobility, static and dynamic labor demand, unions, discrimination, government intervention in labor markets, and "hedonic" equilibria in labor markets.
• ECON 516 Empirical Microeconomics (3 credits): Provides an overview of methods used in empirical microeconomic research, drawing examples from health economics, law and economics, and business economics. Emphasis is placed on designing econometric and statistical analyses to test economic hypotheses, with class projects expanding on analyses from previously published studies.
• ECON 517 Empirical Industrial Organization II (3 credits): Examines economic models of competition and industry structure, including models of demand, supply, investment, and entry. Special attention is paid to econometric modeling of industries and the use of price and game theory in industrial organization. Matching and market design are also covered.
• ECON 518 International Macroeconomics (3 credits): This course covers the effects of fiscal and monetary policies on exchange rates and the current account and balance of payments. It includes discussions on exchange market efficiency, exchange rates and prices, LDC debt, and policy coordination.
• ECON 519 Economic Growth and Development (3 credits): This course involves the mathematical and statistical analysis of topics in microeconomic development and an introduction to frequently used applied econometric methods. Topics covered include poverty, inequality, health, education, fertility, marriage markets, and other gender issues, with a particular focus on intra-household bargaining models and their applications.
• ECON 521 Matching and Market Design (3 credits): This course begins with an overview of different matching markets (e.g., one-to-one or many-to-one, with or without transfers, centralized or decentralized) and the standard empirical models. It then provides an in-depth discussion of theoretical and empirical market design for school choice and kidney transplants.
• ECON 522 Public Economics: Tax Policy (3 credits): This course studies the effects of taxation on individual and firm behavior, general equilibrium tax incidence analysis, optimal taxation theory, optimal implementation of tax reform, and the analysis of comprehensive income and consumption taxes.
• ECON 523 Dynamic Optimization (3 credits): This course involves the study of dynamic optimization in discrete and continuous time, including numerical methods and applications to macroeconomics, finance, and resource and energy economics.
• ECON 547 Advanced Topics in Energy Economics (3 credits): This course provides a detailed development and analysis of topics in energy modeling. Topics include optimal extraction of depletable resources, models of storable energy commodities, energy demand by the end-use sector, models of non-competitive behavior, energy security, and the relationship between energy and commodity prices.
• ECON 565 Health Economics (3 credits): This course applies empirical and theoretical economic models to health and healthcare. It includes discussions on production, cost, demand and supply factors, payment methods, and the effects of regulation. Specific topics include the optimal design of health insurance markets, cost-benefit analysis of healthcare technologies, econometric evaluation of government regulations and reimbursement in the healthcare sector, and the testing of hypotheses that explain rising prices and costs of healthcare.
• ECON 575 Topics in Macroeconomics I (3 credits): This course involves discussions on selected topics in macroeconomics.
• ECON 576 Topics in Macroeconomics II (3 credits): This course covers the aggregate implications of quantitative models of producer heterogeneity, with applications to firm dynamics, innovation and aggregate growth, cross-country income differences, and international trade.
• ECON 579 Topics in Econometrics II (3 credits): This course discusses selected topics in advanced econometrics, focusing on the mathematical and statistical modeling of phenomena such as extended panel data methods, spatial econometrics, bootstrapping, factor models, wavelets, smoothing-splines, sieves, model averaging, continuous and discrete dynamic programming models, econometrics of auctions, BLP methods of demand estimation, structural and non-structural models of producer behavior, point and set identification, and Bayesian Econometrics/Metropolis-Hastings MCMC algorithms.
• SOPE 502 Applications in Program Evaluation - Criminal Justice (3 credits): This course introduces students to the program evaluation literature in criminal justice. It examines reform projects underway at every stage of the American criminal justice system, emphasizing the crucial understanding of their impact for the future of criminal justice in the United States. The course studies policies and interventions at various stages, from policing to reintegration.
• SOPE 504 Applications in Program Evaluation - Labor (3 credits): This course introduces students to the program evaluation literature in labor markets. Students will engage in critical reading of existing evaluations of labor market policies, assess them for various types of validity and generalization, apply empirical tools based on methodological best practices, identify and access essential datasets commonly used in influential employment-related research, and discuss policies with professionals regarding the salience, outcomes, workings, and broader context of public programs designed to improve labor markets.
• SOPE 506 Applications in Program Evaluation - Health (3 credits): This course provides a framework for analyzing the evidence base for public health programs and interventions. It helps students understand how such programs and interventions can impact health policy and affect the health of populations and individuals, recognizing the crucial role of public health and healthcare service delivery in shaping population health and influencing health systems at all levels.
• SOPE 508 Applications in Program Evaluation - Early Childhood Development (3 credits): This course introduces students to the program evaluation literature in early childhood education. It examines the evidence on the extent to which early childhood education program offerings have long-term impacts on later success, considering significant recent investments in this area. The course also explores the policy contexts of early childhood education and discusses the importance of using evidence to drive decision-making at all policy levels, from school districts to the U.S. Department of Education, and the role of family in children's educational experiences.