ECON 600 Mathematics for Economists0.50 units
This course is designed to review the basic mathematical background that incoming masters and doctoral students will need for their core economics and econometrics courses.
33 graduate courses in the Fall 2026 Graduate Calendar.
This course is designed to review the basic mathematical background that incoming masters and doctoral students will need for their core economics and econometrics courses.
This course studies models of economic decision making. Topics include: choice under uncertainty, consumer and producer theory and game theory.
The main purpose of this course is to provide students with the methodological tools that underlie dynamic equilibrium analysis of the macro-economy.
This course considers some of the main problems of macroeconomics and addresses their policy implications.
This course for MA students examines the monetary and financial aspects of the macro-economy. Its main purpose is to prepare students to conduct analyses of problems and policies in monetary economics. Topics may include the link between monetary policy and output, the economic cost of aggregate fluctuations, the costs and benefits of price stability, the role of central banks and the banking system.
This course focuses on increasing students' understanding of the roles of economic theory and empirical methods and on the development of skills in the critical analyses of economic research. Through reading a variety of research papers and attending seminars, students will gain exposure to different research methodologies used in economics. Students will learn how to synthesize and critique research on a particular topic by writing reviews of academic papers and/or research reports from government and non-governmental agencies, as well as by writing a paper such as literature survey on an assigned topic. Students will enhance their writing skills and will also gain practice in presenting a research paper.
This course covers the techniques, and associated coding, required to compile, manage, manipulate and describe economic data. Topics may also include recent data generation and data description techniques including data visualization, text analysis, unsupervised learning techniques, and an introduction to numerical analysis.
Specification and estimation of the linear regression model. Departures from the Gauss-Markov assumptions include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include generalized least squares, and simultaneous equations/instrumental variables. They may also include nonlinear regression, and limited dependent variable models. Some or all of the problem sets involve working with the computer.
This course provides an introduction to the analysis of microdata - data in which the unit of analysis in an individual decision-maker, such as a consumer, worker, or firm. The course familiarizes students with the various types of microdata structures and draws links between theoretical economic models and statistical models. It devotes considerable attention to analytical techniques for describing univariate and bivariate distributions, including the use of kernel density estimators and scatterplots. The course addresses statistical inference using multivariate regressions, covering a series of techniques and issues of particular significance in the analysis of microdata. Examples include Monte Carlo methods; bootstrapping; the use of sampling weight; omitted variable bias; measurement error; and instrumental variables. Completion of an empirical project is a course requirement.
This course introduces students to methods and practices of analyzing data that exhibit dependence over time. The topics may cover fundamental linear time series models (autoregressive processes, moving average processes and their generalizations), modelling time-varying volatility (the autoregressive conditional heteroskedasticity or ARCH family of models and stochastic volatility models), multivariate time series analysis, non-stationary models, and ongoing topics in modern time series econometric research.
The course covers important topics related to scientific computing through applications in either microeconomics, macroeconomics or economics. The topics include: floating point arithmetic, nonlinear equations, optimization, numerical derivatives and numerical integration, differential equations, and simulation of dynamic models.
This course explores a variety of machine learning methods used to analyze complex economic data and inform policy decisions. The course introduces students to predictive methods and exploratory data analysis for high-dimensional data. These methods, such as decision trees and neural networks, are applied to observational data with an emphasis on the analysis of large and/or complex datasets of interest to social scientists and policy makers
Estimation of econometric models using automatic model selection and dimension reduction techniques, with emphasis on estimation of the causal effects of economic policies. Methods may include Lasso and related estimators, principal components, regression trees or forests, and double/debiased machine learning. These methods may be used with conditional ignorability assumptions in linear models, directed acyclic graphs, or structural equation models to estimate heterogeneous treatment effects with economic data.
This course presents classical and new international trade theories and discusses selected topics representative of recent empirical research. Theory, extensions, applications and empirical tests of models such a Ricardian, Hecksher-Ohlin, increasing returns to scale and imperfect competition, political economy, heterogeneous agents (Melitz) will be covered. Additional elective topics include trade and environment; economic geography; trade, aid and development; trade and conflict; trade and growth.
This course demonstrates the usefulness of economic analysis to the study of global governance. Topics include the economic analysis of international trade, foreign direct investment, and international finance. Students with more advanced economics background (as a minimum, at least one economics course above the 100 level that focused on international economics or an equivalent applied course such as development economics, environmental economics) are recommended to replace 637 with one of a list of courses, including internationally oriented economics courses, some of the PSCI international political economy courses, or Faulty of Environment courses that include significant international political economy content.
This course studies the economic role of the public sector in a market economy. Topics may include the provision of public goods, externalities and public choice, analysis of the growth in government spending, the design of tax systems, international aspects of taxation, and fiscal federalism.
This course introduces students to the role of economics in health care and health policy. It is meant to be a survey of major topics in health economics and an introduction to the ongoing debate over health care policy. Topics include the economic determinants of health and health policy, the market for medical care, the market for health insurance, and the role of the government in health care, and health care reform.
This course studies firm behaviour in markets that are imperfectly competitive and the differences in industry equilibrium relative to perfect competition. Topics may include market structure, the theory of the firm, differentiated products, price discrimination, advertising, barriers to entry, and strategic behaviour as well as policy implications.
This course studies the incentives to innovate and how institutions and public policy shape these incentives. Topics may include the nature of ideas and innovation, intellectual property rights, technology clusters, knowledge spillovers and diffusion, whether immigration contributes to innovation, and the impact of artificial intelligence.
Labour market structure and operations. Theories of real wages, labour supply, employment-unemployment, and labour unions.
The economics of renewable and non-renewable resources in a Canadian context. Problems peculiar to the fisheries, forestry, mineral industries and oil and gas production and consumption are analyzed. Also considered are economic and constitutional issues arising from the uneven distribution of resource rents in Canada.
This course will review the application of the tools and theory of economics to environmental problems. The normative foundations of economic analysis will be discussed including efficiency, intergenerational equity and sustainability. The design and implementation of environmental policy will be analyzed including the use of command and control regulation, market-based instruments, and legal liability. Applications to various environmental issues may include global warming, trans-boundary pollution, and trade and the environment.
The course combines water economic theory and practice, addressing major global water challenges such as climate change, water scarcity, flood risks, water quality, and resource recovery in a circular economy. Particular attention will be paid to water resource valuation and water pricing. Students will play water games and write an economic assessment paper about the costs and benefits of water, food and energy security related to large-scale water infrastructure such as dam building in a transboundary river basin.
This course considers the application of option concepts from finance to value real assets. The focus is on using real options theory and methodology to value investments characterized by uncertainty, irreversibility, and flexibility in the timing of irreversible expenditures. The course begins with an introduction to stochastic processes, Ito's Lemma, the Black-Scholes equation, contingent claims analysis and dynamic programming. Methods to solve simple option value problems will be presented, such as binomial trees and Monte Carlo simulation. Applications will focus on problems in natural resource and environmental economics, such as valuing the option to drill for oil or install pollution control equipment and, time permitting, other applications in economics.
Topics covered include: expected utility theory, no-arbitrage pricing, equilibrium-pricing models, derivative-security pricing, asymmetric information, capital structure theory and dividend policy.
One or more half-courses will be offered at different times as announced by the Department.
This course builds on the models and the techniques developed in Microeconomic Theory I. Topics may include general-equilibrium analysis, game theory, the economics of information, mechanism design and applications of bargaining theory to the theory of markets.
The main purpose of this advanced course is to prepare PhD students to conduct research to conduct research in macroeconomics.
This is a research-oriented course in macroeconomics. Topics may include issues in economic growth and development, economic inequality, fiscal policy, labour market policy, and business cycles.
This course for PhD students examines the monetary and financial aspects of the macro-economy. Its main purpose is to prepare students to conduct analyses of problems and policies in monetary economics. Topics may include the link between monetary policy and output, the economic cost of aggregate fluctuations, the costs and benefits of price stability, the role of central banks and the banking system.
The course provides a rigorous treatment of more advanced topics in econometrics. They include system of equations, simultaneous equations, generalized method of moments, empirical likelihood, vector autoregression and dynamic models, time series models and methods, discrete dependent variables, and limited dependent variables.
This course reviews identification strategies used in the analysis of cross-sectional and longitudinal microdata. Techniques primarily come from applications in labour economics, health economics and industrial organization. These may include fixed and random effects models, duration analysis, instrumental variables estimators, nonparametric and semi-parametric analysis, decomposition techniques, quantile regression, difference-in-difference estimators, propensity score matching and regression discontinuity design. The primary objectives of the course are to familiarize students with current methods used in microeconometric research and provide them with hands-on experience applying these methods using an appropriate statistical software package.
This course covers some of the most important concepts, models and methods used in the empirical analysis of macroeconomic problems. In particular the course covers established time series techniques as well as more recent developments such as testing for unit roots, measurement of the persistence of shocks and estimation and hypothesis testing in cointegrated systems. Several dynamic models will be studied using time series analysis methods. Topics covered include basic concepts in time series analysis (modelling volatility and trend), VAR Modes (including structural VAR's), VECM and Cointegration, neural networks and estimation of DSGE models. During the course, students will have the opportunity to explore several case studies using econometric software such as RATS and MATLAB.