Jukina Hatakeyama

Lecture Notes / Econometrics I — TA Sessions (2025)

Course materials

Econometrics I — TA Sessions (2025)

Selected TA-session materials on sampling theory, maximum likelihood, matrix methods, GLS, and time-series processes.

  • Academic year: 2025
  • Term: Spring & Summer
  • University: The University of Osaka, Graduate School of Economics
DateSessionTopicsMaterials
Sessions 1–7 1–7
Sessions led by another teaching assistant
  • These sessions were taught by Minamoto. No handouts by Jukina Hatakeyama are listed here.
Archive noteNo PDF is included for these sessions.
8
Sampling theory and estimation methods
  • Population and sample
  • Law of large numbers and central limit theorem
  • Sampling distribution of the sample variance
  • OLS versus maximum likelihood
  • Parametric and non-parametric estimation
Probability & asymptoticsOLS versus MLE
9
M-estimation and maximum likelihood
  • M-estimation and extremum estimators
  • Fisher information and the Cramér-Rao lower bound
  • Consistency and asymptotic normality of the MLE
  • Newton-Raphson and scoring methods
  • MLE in simple and multiple regression
M-estimation & MLE
10
Positive definite matrices and generalised least squares
  • Positive definite and positive semidefinite matrices
  • Cholesky decomposition
  • Variance-covariance matrices and quadratic forms
  • GLS under heteroskedasticity and correlated errors
  • Comparison of OLS and GLS variances
Matrix foundationsGLS
11
ARMA processes
  • Introduction to autoregressive and moving-average processes
Available on requestNo public PDF is included in this repository.
12
Assignment solutions and course revision
  • Solutions to Assignment 1
  • Revision of the main lecture material
Board notes onlyNo PDF handout was published.