Ben Lambert

Using bees to demonstrate the importance of overdispersed Markov chains in MCMC

Central Limit Theorem

Central Limit Theorems: An Introduction

An introduction to central limit theorems

Variance-covariance matrix using matrix notation of factor analysis

Central Limit Theorem - proof part 3

Central Limit Theorem - proof part 4

Central Limit Theorem - proof part 2

Central Limit Theorem - proof part 1

R squared part 1

Unbiasedness of OLS - part one

Omitted variable bias - proof part 1

Least Squares Estimators as BLUE

Least Squares Estimators - in summary

An introduction to continuous probability distributions.

Ordinary Least Squares Estimators - derivation in matrix form - part 1

Estimated variance of OLS estimators - intuition behind maths

Variance of Least Squares Estimators - Matrix Form

Why is a likelihood not a probability distribution?

Omitted variable bias - proof part 2

An introduction to the Bernoulli and binomial distributions

Unbiasedness vs consistency of estimators - an example

Why maximise log likelihood?

Maximum Likelihood estimation - an introduction part 1

Least Squares as an unbiased estimator - matrix formulation

How to do integration by sampling

The Gauss-Markov Theorem proof - matrix form - part 1

The Gauss-Markov Theorem proof - matrix form - part 2

Linearity in parameters - Gauss-Markov

Asymptotic assumptions for time series least squares

Variance of OLS estimators in the presence of serial correlation

Random variables and probability distributions.

Gauss-Markov proof part 1 (advanced)

Heteroscedasticity: as symptom of omitted variable bias - part 2

Heteroskedasticity summary

Gauss-Markov assumptions part 1

Omitted variable bias - example 1

An introduction to the Random Walk Metropolis algorithm

An introduction to continuous marginal probability distributions

An Introduction to the Asymptotic Behaviour of Estimators

Gauss Markov conditions summary of problems of violation

The duality of meaning for likelihoods and probability distributions: the equivalence principle

How to derive a Gibbs sampling routine in general

How to differentiate with respect to a vector - part 1

An introduction to inverse transform sampling

An introduction to the concept of a sufficient statistic

Kronecker Matrix Product - properties

Pooled cross sectional models introduction

Omitted variable bias - example 1 (old)

Omitted variable bias - example 3

Pooled cross sectional data benefits - part 1