- ECON 186 Mathematical Methods for Economic Analysis
Presents mathematical methods commonly used in graduate-level economic analysis: basic matrix algebra, real analysis, functions, continuity concepts, differentiation, Taylor expansions, and implicit function theorem and optimization.
- ECON 200 Microeconomic Analysis
Survey of partial equilibrium analysis, market distortions, consumer choice and production and trade theory, perfect and imperfect competition, price discrimination, and intertemporal choice theory.
- ECON 294A Applied Economics and Finance Laboratory
Practical experience in managing computerized data sets and running statistical packages. Covers STATA, Python, R.
- ECON 202 Macroeconomic Analysis
Aggregate economic analysis: determinants of aggregate expenditures and output, the roles of monetary and fiscal policy, recent developments in macro theory; macro policy issues.
- ECON 294B Applied Economics and Finance Seminar
Bi-weekly seminars designed to present students with current working applications in various fields of applied economics and finance.
- ECON 216 Applied Econometric Analysis I
The use of statistical techniques for the testing of economic hypotheses and the estimation of parameters, with emphasis on regression analysis. Includes methods of dealing with serial correlation, errors in variables, multicollinearity, and heteroscedasticity. Experience with common statistical packages. Enrollment is restricted to applied economics and finance graduate students.
- ECON 217 Applied Econometric Analysis II
Covers discrete and limited dependent variable models, nonparametric estimation methods, resampling methods, as well as statistical (machine) learning methods commonly used in industry. Students learn the theoretical foundations behind the methods and also how to apply them in practice. . Enrollment is restricted to applied economics and finance graduate students.
- ECON 211C
This course is a foundational time series course for doctoral students in Economics. We will begin by covering the foundations of univariate linear time series models and will then discuss their estimation and use for forecasting. We will subsequently move to multivariate time series models (Vector Autoregressions) and methods for dealing with cointegration. Along the way, we will also cover models for heteroskedasticity, state space models and the Kalman filter as well as Markov Chain Monte Carlo methods. Extensive use of applications and examples will be made using financial data and the R statistical computing environment. Homework assignments will be designed to apply the methods learned in lecture to actual data - i.e. computing will be an essential component of the assignments.
- AMS206 Applied Bayesian Statistics
Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, ...), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models). Prerequisite(s): course 131 or 203, or by permission of the instructor. Enrollment is restricted to graduate students except by instructor permission.
- ECON 236 Financial Engineering
This course surveys the financial risks faced by corporation, banks, and other financial institutions that arise from changes in interest rates, foreign exchange rates, commodity prices, and stock prices. It examines the characteristics, payoffs, and pricing of financial derivatives and other instruments for managing risk, including options, forwards, futures, swaps, structured notes, and asset-backed securities. Several cases will be used to illustrate how actual firms solve financial risk management problems.
- ECON 235 Corporate Finance
Application of modern financial theory to corporate decision making. Topics covered include capital budgeting and the firm's investment decision, capital structure, dividend policies, and the implications of corporate governance for enterprise financial goals.
- ECON 201 Applications in Microeconomics
Applies concepts and tools developed in ECON 200 to problems encountered in private- and public-sector output and labor markets. The focus is empirical; topics include analysis of labor supply and labor demand and the role of government labor market policies, analysis of pricing policies and regulation, estimation of the returns to schooling, estimation of demand and cost functions, and the role of unions in the economy. ECON 200 is strongly recommended as preparation.
- Research Methods Resources
- Exploring Economics
- Stanford Institute for Economic Policy Research (SIEPR)
- Computation and Economics
- Mathematical and Quantitative Methods in Economics
- J Doyne Farmer
- Victor Yakovenko
- Judea Pearl
- Matheus Grasselli
- Amartya Sen
- Robert Solow
- Paul Samuelson
- Yanis Varoufakis
- Nicholas Economides
- Dan Friedman
- Hal Varian
- Omer Tamu
- Adam Wierman
- Elchanan Mossel
- Raj Chetty
- Susan Athey
- Matthew O. Jackson
- Angus Deaton
- Joseph Stiglitz
- Steven Pressman
- Matthew Baron
- Thomas Sargent
- Sanjeev Goyal
- Paul Romer
- James Heckman
- Christopher Sims
- Kenneth Arrow
- Bill Mitchell
- Christopher Knittle
- Jonas Peters
- Markus K. Bunnermeier
- Owen Zidar
- Wei Kang
- Leigh Tesfatsion
- Oliver Hart
- Paul Milgrom
- Steven Keen
- Art Owen
- Benjamin Golub
- Lars Vilhuber
- Reihnard Selten
- Hyman Minsky
- Talcott Parsons
- Robert Hodrick
- Joshua Angrist
- David Card
- Guido Imbens
- John Cochrane
- Drew Fudenberg
- Abhijit Banerjee
- Esther Duflo
- Michael Kremer
- Franco Modigliani
- James Tobin
- Friedman
- Keynes
- Herbert A. Simon
- Matthew O. Jackson
- Brian Arthur
- Jozef Barunik (Networks)
- Leeat Yariv
- Leigh S Tesfatsion
- Robert Hanneman