• Meeting day/time: T,Th 12:00-1:20pm, Saieh Hall, Room 247
• Office hours also available by appointment

Advanced undergraduate or first-year graduate microeconomic theory, statistics, linear algebra, multivariable calculus, recommended coding experience.

• Davidson, Russell and James G. MacKinnon, Econometric Theory and Methods, Oxford University Press (2004).
• Hansen, Lars Peter and Thomas J. Sargent, Robustness, Princeton University Press (2008).
• Scott, David W., Multivariate Density Estimation: Theory, Practice, and Visualization, 2nd edition, John Wiley & Sons (2015).
• Wolpin, Kenneth I., The Limits of Inference without Theory, MIT Press (2013).

The purpose of this course is to give students experience estimating parameters of structural models. We will define the respective differences, strengths, and weaknesses of structural modeling and estimation versus reduced form modeling and estimation. We will focus on structural estimation. Methods will include taking parameters from other studies (weak calibration), estimating parameters to match moments from the data (GMM, strong calibration), simulating the model to match moments from the data (SMM, indirect inference), maximum likelihood estimation of parameters, and questions of model uncertainty and robustness. We will focus on both obtaining point estimates as well as getting an estimate of the variance-covariance matrix of the point estimates.

Some of the examples in the course will come from economics, but the material will be presented in a general way in order to allow students to apply the methods to estimating structural model parameters in any field. We will focus on computing solutions to estimation problems. Students can use whatever programming language they want, but I highly recommend you use Python 3.x (Anaconda distribution). I will be most helpful with code debugging and suggestions in Python. We will also study results and uses from recent papers listed in the "References" section below. The dates on which we will be covering those references are listed in the "Daily Course Outline" section below.

• You will learn the difference between and the strengths and weaknesses of:
  • Structural vs. reduced form models
  • Linear vs. nonlinear models
  • Deterministic vs. stochastic models
  • Parametric vs. nonparametric models
• You will learn multiple ways to estimate parameters of structural models.
  • Calibration
  • Maximum likelihood estimation
  • Generalized method of moments
  • Simulated method of moments
• You will learn how to compute the variance-covariance matrix for your estimates.
• You will learn coding and collaboration techniques such as:
  • Best practices for Python coding (PEP 8)
  • Writing modular code with functions and objects
  • Creating clear docstrings for functions
  • Collaboration tools for writing code using Git and GitHub.com.

Grades will be based on the four categories listed below with the corresponding weights.

• Homework: I will assign 5 problem sets throughout the term.
  • You must write and submit your own computer code, although I encourage you to collaborate with your fellow students. I DO NOT want to see a bunch of copies of identical code. I DO want to see each of you learning how to code these problems so that you could do it on your own.
  • Problem set solutions, both written and code portions, will be turned in via a pull request from your private GitHub.com repository which is a fork of the class master repository on my account. (You will need to set up a GitHub account if you do not already have one.)
  • Problem sets will be due on the day listed in the Daily Course Outline section of this syllabus (see below) unless otherwise specified. Late homework will not be graded.
• Project: The project will either be a replication of an existing structural estimation paper or an original estimation project. I will approve each project. The final writeup of the project will be worthIt will be worth 20 points, which is equivalent to two homework assignments. The initial in-class presentation of your project proposal and your final in-class presentation of your project results will each be worth 5 points. The project write up will be due on Wednesday, March 8, the day after regular classes end (first reading day).

• Adda, Jerome and Russell Cooper, Dynamic Economics: Quantitative Methods and Applications, MIT Press (2003)
• Altonji, Joseph G., Anthony A. Smith, Jr., and Ivan Vidangos, "Modeling Earnings Dynamics," Econometrica, 84:4, pp. 1395-1454 (July 2013)
• Brock, William A. and Leonard J. Mirman, "Optimal Economic Growth and Uncertainty: The Discounted Case," Journal of Economic Theory, 4:3, pp. 479-513 (June 1972)
• Davidson, Russell and James G. MacKinnon, Econometric Theory and Methods, Oxford University Press (2004)
• Duffie, Darrell and Kenneth J. Singleton, "Simulated Moment Estimation of Markov Models of Asset Prices", Econometrica, 61:4, pp. 929-952 (July 1993)
• Fuhrer, Jeffrey C. and George R. Moore, and Scott D. Schuh, "Estimating the Linear-quadratic Inventory Model: Maximum Likelihood versus Generalized Method of Moments," Journal of Monetary Economics, 35:1, pp. 115-157 (Feb. 1995).
• Gourieroux, Christian and Alain Monfort, Simulation-based Econometric Methods, Oxford University Press (1996)
• Hansen, Lars Peter, "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, 50:4, pp.1029-1054 (July 1982)
• Hansen, Lars Peter and Kenneth J. Singleton, "Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models", Econometrica, 50:5, pp. 1269-1286 (September 1982)
• Keane, Michael P., "Structural vs. Atheoretic Approaches to Econometrics," Journal of Econometrics, 156:1, pp. 3-20 (May 2010).
• Laroque, G. and B. Salanie, "Simulation Based Estimation Models with Lagged Latent Variables", Journal of Applied Econometrics, 8:Supplement, pp. 119-133 (December 1993)
• Lee, Bong-Soo and Beth Fisher Ingram, "Simulation Estimation of Time Series Models", Journal of Econometrics, 47:2-3, pp. 197-205 (February 1991)
• McDonald, James B., "Some Generalized Functions for the Size Distribution of Income," Econometrica 52:3, pp. 647-665 (May 1984)
• McDonald, James B. and Yexiao Xu, "A Generalization of the Beta Distribution with Applications," Journal of Econometrics, 66:1-2, pp. 133-152 (March-April 1995)
• McDonald, James B., Jeff Sorensen, and Patrick A. Turley, "Skewness and Kurtosis Properties of Income Distribution Models," Review of Income and Wealth, 59:2, pp. 360-374 (June 2013)
• McFadden, Daniel, "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, 57:5, pp. 995-1026 (September 1989)
• Newey, Whitney K. and Kenneth D. West, "A Simple, Positive, Semi-definite, Heteroskedasticy and Autocorrelation Consistent Covariance Matrix," Econometrica, 55:3, pp. 703-708 (May 1987)
• Rust, John, "Comments on: 'Structural vs. Atheoretic Approaches to Econometrics' by Michael Keane," Journal of Econometrics, 156:1, pp. 21-24 (May 2010).
• Smith, Anthony A. Jr., "Indirect Inference," New Palgrave Dictionary of Economics, 2nd edition, (2008).

If you need any special accommodations, please provide us with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.