Advanced Microeconometrics

ECS556 Advanced Microeconometrics

  • Topics

    Topics

    The objective of the course is to introduce students to topics in labor economics as well as contemporary empirical research in this field.

    The course is divided four main themes:

    • Count Regression : Poisson, negative binomial, hurdle, zero-inated, mixtures, endogeneity, panel data
    • Clustered Data : OLS with cluster-robust standard errors, feasible GLS, serially correlated errors, random effects, mixed models; bootstrap without asymptotic refinement, fixed effects, what to cluster over, twoway clustering, spatial correlation, few clusters, bootstrap with asymptotic refinement, nonlinear models, endogenous regressors
    • Simulation : Pseudo random draws, Monte Carlo integration, Gaussian quadrature, Monte Carlo experiment, Maximum simulated likelihood, Bayesian approach, Bayesian analytical example
    • Nonparametric and Semiparametric Estimation

    The various methods will be illustrated using Stata.

  • Learning outcome

    Learning outcome

    Knowledge - at the end of the course students will:

    • have a sound knowledge on key topics and state of the art methods in advanced microeconometrics

    Skills - at the end of the course students will:

    • be able to apply these new methods to data

    General Competence - at the end of the course students will:

    • be able to integrate the new knowledge into their thesis work

  • Teaching

    Teaching

    Lectures, Stata labs and student presentations.

  • Required prerequisites

    Required prerequisites

    Successful completion of an introductory econometrics course on the doctoral level. Presumed background: Maximum likelihood estimator, nonlinear least squares estimator, asymptotic theory for m-estimators, statistical inference, gradient methods, computation of marginal effects, nonlinear GMM.

  • Requirements for course approval

    Requirements for course approval

    Participation in class

  • Assessment

    Assessment

    Term paper

  • Grading Scale

    Grading Scale

    Pass/fail

  • Computer tools

    Computer tools

    Stata

  • Semester

    Semester

    Fall 2017 (Aug. 28-Sept. 1)

  • Literature

    Literature

    The main references will be

    • A.C. Cameron and P.K. Trivedi (2005), Microeconometrics: Methods and Applications, Cambridge University Press.
    • A.C. Cameron and P.K. Trivedi (2005), Microeconometrics using Stata.
    • A.C. Cameron and D.L. Miller (2015) ¿A Practitioner's Guide to Cluster-Robust Inference." Journal of Human Resources, 50(2), pp. 317-372.
    • Plus some relevant papers.

Overview

ECTS Credits
5
Teaching language
English.
Semester
Autumn

Course responsible

Lecturer: A. Colin Cameron, University of California - Davis

Course responsible: Aline Bütikofer and Kjell Salvanes