Lagged dependent variable ARMA

Koyck transformation

Invertibility - converting an MA(1) to an AR(infinite) process

ARMA(1,1) processes - introduction and examples

The partial adjustment model

Error correction model - part 1

Error correction model - part 2

Pooled cross sectional models introduction

Pooled cross sectional data benefits - part 1

Pooled cross sectional data benefits - part 2

Panel vs pooled data

Panel data econometrics - an introduction

First Differences estimator

Fixed Effects estimators: an introduction

Least Squares Dummy Variables estimators

Estimating unobserved heterogeneity

The concept of R-Squared in Fixed Effects and LSDV estimators

Fixed Effects, First Differences and Pooled OLS - intuition

Fixed Effects and First Differences comparison - part 1

Fixed Effects and First Differences comparison - part 2

Fixed Effects and First Differences comparison - part 3

Random Effects Estimator - an introduction

How does Random Effects work?

Random Effects estimators as fGLS

Random Effects estimators - time-invariant variables effects benefit

Random Effects vs Fixed Effects estimators

Hausman test for Random Effects vs Fixed Effects

Panel data conditions for consistency and unbiasedness of estimators

Panel data conditions for BLUE estimation and inference

The linear probability model - an introduction

The linear probability model - example

The problems with the linear probability model - part 1

The problems with the linear probability model - part 2

The problems with the linear probability model - part 3

Nonlinear discrete choice models - an introduction

Discrete choice models - introduction to logit and probit

Discrete choice models - partial effect part 1

Discrete choice models - partial effect part 2

Nonlinear discrete choice model estimation

Maximum Likelihood estimation - an introduction part 1

Maximum Likelihood estimation - an introduction part 2

Maximum Likelihood estimation - an introduction part 3

Why maximise 'log' likelihood?

The Cramer-Rao Lower Bound: Inference in Maximum Likelihood Estimation

Maximum Likelihood - Cramer Rao Lower Bound Intuition

Likelihood ratio test - introduction

Wald Test - introduction

Score test (Lagrange Multiplier test) - introduction

Maximum Likelihood estimators of population mean and variance - part 1

Maximum Likelihood estimators of population mean and variance - part 2

Maximum Likelihood estimators of population mean and variance - part 3

Maximum likelihood: Normal error distribution - estimator variance part 1

Maximum likelihood: Normal error distribution - estimator variance part 2

Maximum likelihood: Normal error distribution - estimator variance part 3

Maximum Likelihood: Bernoulli random variables estimator variance part 1

Maximum Likelihood: Bernoulli random variables estimator variance part 2

Least Squares as a Maximum Likelihood estimator

Least squares comparison with Maximum Likelihood - proof that OLS is BUE

Maximum Likelihood estimation of Logit and Probit

Log odds interpretation of logistic regression

Probit model as a result of a latent variable model

Simultaneous equation models - an introduction

Simultaneous equation models - reduced form and structural equations

Simultaneous equation models - parameter identification

Simultaneous Equation Models: Order condition for parameter identification

Monte Carlo Simulation for estimators: An Introduction

Monte Carlo Simulation for Ordinary Least Squares

Monte Carlo Simulation of Omitted Variable Bias in Least Squares

Testing for significance of correlation

One sample t test on mean

Independent two sample t test for populations with equal variances

Variance Inflation Factors: testing for multicollinearity