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