Course Detail

Statistical Inference (41902)

Course Description by Faculty

  • Xiu, Dacheng
  • Content
    This Ph.D.-level course is the second in a two-quarter sequence with Business 41901. The central topic is statistical inference using asymptotic approximations. We will cover linear regression models, generalized method of moments, time series. Time permitting; we will discuss factor models.
    Format
    • Lectures

  • Prerequisites
    Business 41901
  • Materials
    Three recommended texts are Econometrics by Hayashi, Econometric Analysis of Cross Section and Panel Data by Wooldridge, and Time Series Analysis by Hamilton. Asymptotic Theory for Econometricians (Revised Edition) by White provides a useful and concise reference on asymptotic results.
    Resources
    • Canvas Site Available

  • Grades
    Based on homework assignments and an in-class final exam. Cannot be taken pass/fail. No provisional grades.
    Grades
    • Graded homework assignments

    • Graded attendance/participation

    Assessment & Testing
    • Final exam (in class)

    Restrictions
    • No pass/fail grades

  • Syllabus
  • Winter 2022Section: 41902-50F 9:00AM-12:00PMHarper CenterC06In-Person Only
Description and/or course criteria last updated: November 4 2021