Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

meco7312

Supplementary codes for MECO7312, Advanced Statistics and Probability

.Rmd (R Markdown) using RStudio.

.ipynb (Python Jupyter Notebook) using Google Colab.

  • Lecture 2 (lecture2.Rmd, L2_.ipynb): Sampling from a scalar random variable using probability integral transformation.

  • Lecture 4 (lecture4.Rmd, L4_.ipynb): Gibbs sampling, sampling from a multivariate Normal.

  • Lecture 5 (lecture5.Rmd, L5_.ipynb): Sampling distributions of estimators. Order statistics.

  • Lecture 6 (lecture6.Rmd, L6_.ipynb): Asymptotics. Central Limit Theorem. Delta Method

  • Lecture 7 (L7_.ipynb): Generalized Method of Moments. Optimal 2-steps GMM.

  • Lecture 8 (L8.ipynb): Monte-carlo simulation of bias-variance trade-off

  • Lecture 11 (L11_.ipynb): Likelihood-ratio test. Wilks' Theorem. Power function. Exact and asymptotic tests.

  • L12_bootstrap.ipynb: Using non-parametric bootstrapping to approximate the sampling distribution.

  • Lecture 12 (lecture12.Rmd): Monte Carlo sampling. Importance sampling. Exact and asymptotic tests and power functions.

  • Bootstrap (bootstrap.Rmd): Using non-parametric bootstrapping to approximate the sampling distribution.

  • Lecture 13 (lecture13.Rmd): Implementing the Ordinary Least Squares estimator. Multicollinearity. Omitted variable bias.

  • Lecture 15 (lecture15.Rmd): Variance-covariance of OLS estimator. Heteroskedastic-consistent estimator of the variance-covariance matrix. Clustered standard errors inference.

  • PS3: solutions to Problem Set 3

About

MECO7312 Advanced Statistics and Probability

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published