New Econometric Methods >> Content Detail




This course focuses on recent developments in econometrics, especially structural estimation. The topics include nonseparable models, models of imperfect competition, auction models, duration models, and nonlinear panel data. Results are illustrated with economic applications.


This course has a prerequisite of 14.382 Econometrics and 14.383 Econometrics II.


There are no textbooks for this course.


Students are expected to complete the assigned readings, submit responses to the problem set, and participate in class discussions. They are expected to use MATLAB® to construct their models.

Recommended Citation

For any use or distribution of these materials, please cite as follows:

Whitney Newey, course materials for 14.386 New Econometric Methods, Spring 2007. MIT OpenCourseWare (, Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].

Course Outline

  • Generalized method of moments
    • The GMM estimator
    • Identification
    • Two step optimal GMM estimator
    • Adding moment conditions
    • Asymptotic theory for GMM
    • Conditional moment restrictions
    • Bias in GMM
    • Testing in GMM
    • Small bias methods
  • Nonparametric estimation
    • Empirical distribution functions
    • Kernel density estimators
    • Bias and variance of kernel estimators
    • Consistency and convergence rate of kernel estimators
    • Bandwidth choice for density estimation
    • Multivariate density estimations
    • The curse of dimensionality for kernel estimation
    • Nonparametric regression
    • Kernel regression
    • Series regression
    • Convergence rate for series regression
    • Choosing bandwidth or number of terms
    • Locally linear regression
    • Reducing the curse of dimensionality
    • Estimators with nonparametric components
  • Semiparametric estimation
    • Semiparametric models
    • Semiparametric estimators
    • Consistency and asymptotic normality of minimization estimators
  • Treatment effects
    • Constant treatment effects
    • Random assignment
    • IV identification of treatment effects
    • Random intention to treat
    • The local average treatment effect
    • Selection on observables
    • Regression discontinuity design
  • Nonlinear models in panel data
    • Likelihoods with individual effects
    • Fixed effects and the incidental parameters problem
    • Conditional maximum likelihood
    • Correlated random effects
    • Semiparametric results
    • Fixed effects again
  • Demand estimation with imperfect competition


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