Advanced economic theory, experimentation, econometrics, machine learning and programming skills are fundamental skills any modern economists needs to master, regardless of whether you want to work on the private sector, the public sector, or at a NGO. We cultivate this knowledge in our technically sound curriculum, which contain complex, realistic empirical assignments from the start. Class are held full-time on campus in small classrooms where students get the opportunity to receive highly personalized attention from Faculty.
Take all of the following:
|Course Number & Description||Course Title||Units|
Sets, Number Systems, Proofs, Systems of Linear Equations. Matrix Algebra. Determinants. Linear Independence. Single Variable Calculus. Applications. Integration. Functions of Several Variables. Multivariable Calculus. Implicit Functions. Unconstrained Optimization. Constrained Optimization.
|Essential Mathematics for Economic Analysis||4|
Basics of Probability Theory. Random Variables. Density and Mass Functions. Distributions of a Function of a Random Variable. Expectations. Moments. Moment Generating Functions. Multiple Random Variables. Joint and Marginal Distributions. Conditional Distributions and Independence. Covariance and Correlation. Random Samples. Sampling from the Normal Distribution. Point Estimation. Method of Moments. Maximum Likelihood Estimators. Mean Squared Error. Best Unbiased Estimators. Asymptotic Theory. Law of Large Numbers. Central Limit Theorem. Large Sample Properties of Maximum Likelihood Estimators. Hypothesis Testing and Confidence Intervals.
|Essential Statistics for Econometrics||4|
Preferences and choice, preferences over commodities, consumer demand theory, producer theory, choice under uncertainty, simultaneous and sequential move games, incomplete information games, mechanism and incentive design.
Finite Markov chains, linear state space models, dynamic programming, rational expectations equlibrium, Markov perfect equilibrium, Stackelberg plans, general equilibrium under certainty and uncertainty, Arrow securities, consumption-based asset pricing, incomplete markets.
|Dynamic Stochastic Modeling||4|
The linear regression model. Confidence and prediction intervals. Hypothesis testing. The generalized regression model and heteroscedasticity. Identification and causal inference: randomization, regression, instrumental variables, regression discontinuity, differences in differences.
|Advanced Econometrics I||4|
Maximum likelihood estimation. Binary, multinomial, and ordered discrete response models. Truncated, censored regression. Structural equation modeling. Factor models, filtering and Bayes rule. Random utility and mixed logit models. Demand estimation.
|Advanced Econometrics II||4|
Introduction to computer programming in Python. Jupyter Notebooks. Data Structures. Flow Control. Functions. Debugging. Analysis of Algorithms. Code Optimization. Pandas. String functions. Database Access using SQL. Model Performance and Selection: Cross Validation, Regularization (Lasso/Ridge/Elastic) in linear models. Introduction to Support Vector Machines.
|Computing and Machine Learning for Economics||4|
Potential outcomes framework and causal treatment effects. Unconfoundedness designs, including matching and propensity score methods. Selection on unobservable designs. Quantile regressions. The econometrics of randomized experiments.
|Core Subtotal:||32 units|
13 units from the following:
|Course Number & Description||Course Title||Units|
Regularization, model selection, and supervised and unsupervised learning. Post model selection inference for causal effects. Double/debiased machine learning, causal trees, casual forests, and synthetic controls.
|Machine Learning for Prediction and Causal Inference||4|
Jensen inequality and its implications for decision analysis. From conditional expectations to conditional distributions. Evaluating probabilistic forecasts. Eliciting probabilities from expert opinion. Recovering probabilities from market prices. Correlation and copulas. Linear programming and its many uses. Odds betting, portfolio optimization and the Kelly criterion. Investment under uncertainty. Revenue management. The value of information. Model risk. Implementation and data errors. Interpretation errors. Model mis-specification errors. The precautionary principle. The ethical implications of a decision analysis.
|Evidence-Based Decision Analysis||4|
Competition and Monopoly. Dominant Firms. Collusion and Cartels. Oligopoly. Monopolistic Competition and Market Power. Barriers to Entry. Price Discrimination, Predation and Non-Uniform Pricing.
Ascending, first-price, second-price, and double auctions. Revenue equivalence, multi-unit auctions, the Vickrey-Clarke-Groves mechanism, and matching markets. The deferred acceptance algorithm, the immediate acceptance algorithm, and the many-to-one matching model. 4 lectures.
|Incentives and Market Design||4|
Analysis of the international movement of goods, services, capital and payments. The role of exchange rates, tariffs, quotas, and transport costs. Relationship between international trade and economic growth.
Research methods in labor economics and application of modern empirical techniques to the analysis of labor markets. Topics include labor supply and demand, discrimination, migration, and human capital accumulation.
|Advanced Labor Economics||4|
Economic analysis of pollution, congestion, public good provision, and natural resource conservation. Static and dynamic efficiency, economic growth and sustainability, pollution taxes, marketable permits, and the design of market-based regulations.
|Environmental and Resource Economics||4|
Economic analysis of the rationale for public expenditure and taxation. Externalities, pollution and public policy, income redistribution and public welfare, public goods, collective choice and political institutions, public budgeting techniques and cost-benefit analysis, taxation and tax policy, state-local finance and fiscal federalism.
Advanced topics in economics chosen according to the common interests and needs of the students enrolled.
|Seminar in Economics||1-4|
|Elective Subtotal (minimum):||13 units|
For more details about the courses, see the Cal Poly catalog.
Please note that, except for GSE 522 (Machine Learning for Prediction and Causal Inference), not all elective courses are offered every year. Contact firstname.lastname@example.org for more information.