Undergraduate Econometrics

Embark on a comprehensive journey through econometrics with this 18-hour video course designed for undergraduate students. Begin with the fundamentals, assuming no prior knowledge, and progress to advanced topics in regression analysis. Explore key concepts such as estimators, least squares, Gauss-Markov assumptions, and hypothesis testing. Delve into practical applications with problem sets covering NBA players' wages, presidential election data, and returns to education. Master essential techniques like Weighted Least Squares, Instrumental Variables, and Two Stage Least Squares. Conclude with an in-depth study of time series analysis, covering stationary processes, autoregressive models, and cointegration. Gain a solid foundation in econometric theory and practice, preparing you for advanced studies and real-world applications in economic analysis.

Undergraduate econometrics syllabus.
What is econometrics?.
Econometrics vs hard science.
Natural experiments in econometrics.
Populations and samples in econometrics.
Estimators - the basics.
Estimator properties.
Unbiasedness and consistency.
Unbiasedness vs consistency of estimators - an example.
Efficiency of estimators.
Good estimator properties summary.
Lines of best fit in econometrics.
The mathematics behind drawing a line of best fit.
Least Squares Estimators as BLUE.
Deriving Least Squares Estimators - part 1.
Deriving Least Squares Estimators - part 2.
Deriving Least Squares Estimators - part 3.
Deriving Least Squares Estimators - part 4.
Deriving Least Squares Estimators - part 5.
Least Squares Estimators - in summary.
Taking expectations of a random variable.
Moments of a random variable.
Central moments of a random variable.
Kurtosis.
Skewness.
Expectations and Variance properties.
Covariance and correlation.
Population vs sample quantities.
The Population Regression Function.
Problem set 1 - estimators introduction.
Gauss-Markov assumptions part 1.
Gauss-Markov assumptions part 2.
Zero conditional mean of errors - Gauss-Markov assumption.
Omitted variable bias - example 1.
Omitted variable bias - example 2.
Omitted variable bias - example 3.
Omitted variable bias - proof part 1.
Omitted variable bias - proof part 2.
Reverse Causality - part 1.
Reverse Causality - part 2.
Measurement error in independent variable - part 1.
Measurement error in independent variable - part 2.
Functional misspecification 1.
Functional misspecification 2.
Linearity in parameters - Gauss-Markov.
Random sample summary - Gauss-Markov.
Gauss-Markov - explanation of random sampling and serial correlation.
Serial Correlation summary.
Serial Correlation - as a symptom of omitted variable bias.
Serial Correlation - as a symptom of functional misspecification.
Serial Correlation - caused by measurement error.
Serial correlation biased standard errors (advanced topic) - part 1.
Serial correlation biased standard errors (advanced topic) - part 2.
Heteroskedasticity summary.
Heteroskedastic errors - example 1.
Heteroskedasticity - example 2.
Heteroskedasticity caused by data aggregation (advanced topic).
Perfect collinearity - example 1.
Perfect collinearity - example 2.
Multicollinearity.
Index - where we currently are in the overall plan of econometrics.
Gauss-Markov proof part 1 (advanced).
Gauss-Markov proof part 2 (advanced).
Gauss-Markov proof part 3 (advanced).
Gauss-Markov proof part 4 (advanced).
Gauss-Markov proof part 5 (advanced).
Gauss-Markov proof part 6 (advanced).
Errors in populations vs estimated errors.
Sum of squares.
R squared part 1.
R squared part 2.
Degrees of freedom part 1.
Degrees of freedom part 2 (advanced).
Overfitting in econometrics.
Adjusted R squared.
Unbiasedness of OLS - part one.
Unbiasedness of OLS - part two.
Variance of OLS estimators - part one.
Variance of OLS estimators - part two.
Estimator for the population error variance.
Estimated variance of OLS estimators - intuition behind maths.
Variance of OLS estimators in the presence of heteroscedasticity.
Variance of OLS estimators in the presence of serial correlation.
Gauss Markov conditions summary of problems of violation.
Estimating the population variance from a sample - part one.
Estimating the population variance from a sample - part two.
Problem set 2 - OLS introduction - NBA players' wages.
Hypothesis testing.
Hypothesis testing - one and two tailed tests.
Central Limit Theorem.
Hypothesis testing in linear regression part 1.
Hypothesis testing in linear regression part 2.
Hypothesis testing in linear regression part 3.
Hypothesis testing in linear regression part 4.
Hypothesis testing in linear regression part 5.
Normally distributed errors - finite sample inference.
Tests for normally distributed errors.
Interpreting Regression Coefficients in Linear Regression.
Interpreting regression coefficients in log models part 1.
Interpreting regression coefficients in log models part 2.
The benefits of a log dependent variable.
Dummy variables - an introduction.
Dummy variables - interaction terms explanation.
Continuous variables - interaction term interpretation.
The F statistic - an introduction.
F test - example 1.
F test - example 2.
F test - the similarity with the t test.
The F test - R Squared form.
Testing hypothesis about linear combinations of parameters - part 1.
Testing hypothesis about linear combinations of parameters - part 2.
Testing hypothesis about linear combinations of parameters - part 3.
Testing hypothesis about linear combinations of parameters - part 4.
Confidence intervals.
The Goldfeld-Quandt test for heteroscedasticity.
The Breusch Pagan test for heteroscedasticity.
The White test for heteroscedasticity.
Serial correlation testing - introduction.
Serial correlation - The Durbin-Watson test.
Serial correlation testing - the Breusch-Godfrey test.
Ramsey RESET test for functional misspecification.
Gauss-Markov violations: summary of issues.
Heteroscedasticity: as a symptom of omitted variable bias - part 1.
Heteroscedasticity: as symptom of omitted variable bias - part 2.
Serial correlation: a symptom of omitted variable bias.
Heteroscedasticity: dealing with the problems caused.
Problem set 3 - Presidential election data - hypothesis testing and model selection.
Weighted Least Squares: an introduction.
Weighted Least Squares: mathematical introduction.
Weighted Least Squares: an example.
Weighted Least Squares in practice - feasible GLS - part 1.
Weighted Least Squares in practice - feasible GLS - part 2.
How to address the issue of serial correlation.
GLS estimation to correct for serial correlation.
fGLS for serially correlated errors.
Instrumental Variables - an introduction.
Endogeneity and Instrumental Variables.
Instrumental Variables intuition - part 1.
Instrumental Variables intuition - part 2.
Instrumental Variables example - returns to schooling.
Instrumental Variables example - classroom size.
Instrumental Variables estimation - colonial origins of economic development.
Instrumental Variables as Two Stage Least Squares.
Proof that Instrumental Variables estimators are Two Stage Least Squares.
Bad instruments - part 1.
Bad instruments - part 2.
Bias of Instrumental Variables - part 1.
Bias of Instrumental Variables - part 2.
Bias of Instrumental Variables - intuition.
Consistency of Instrumental Variables - intuition.
Consistency - comparing Ordinary Least Squares with Instrumental Variables.
Inference using Instrumental Variables estimators.
Multiple regressor Instrumental Variables estimation.
Two Stage Least Squares - an introduction.
Two Stage Least Squares - example.
Two Stage Least Squares - multiple endogenous explanatory variables.
Testing for endogeneity.
Testing for endogenous instruments - test for overidentifying restriction.
Problem set 4 - the return to education - WLS and IV estimators.
Time series vs cross sectional data.
Time series Gauss Markov conditions.
Strict exogeneity.
Strict exogeneity assumption - intuition.
Lagged dependent variable model - strict exogeneity.
Asymptotic assumptions for time series least squares.
Conditions for stationary and weakly dependent series.
Stationary in mean.
Spurious regression.
Spurious regression.
Variance stationary processes.
Covariance stationary processes.
Stationary series summary.
Weakly dependent time series.
An introduction to Moving Average Order One processes.
Moving Average processes - Stationary and Weakly Dependent.
Autoregressive Order one process introduction and example.
Autoregressive order 1 process - conditions for stationary in mean.
Autoregressive order 1 process - conditions for stationary in variance.
Autoregressive order 1 process - conditions for Stationary Covariance and Weak Dependence.
Autoregressive vs Moving Average Order One processes - part 1.
Autoregressive vs Moving Average Order One processes - part 2.
Partial vs total autocorrelation.
A Random Walk - introduction and properties.
The qualitative difference between stationary and non-stationary AR(1).
Random walk not weakly dependent.
Random walk with drift.
Deterministic vs stochastic trends.
Dickey Fuller test for unit root.
Augmented Dickey Fuller tests.
Dickey fuller test with time trend.
Highly persistent time series.
Integrated order of processes.
Cointegration - an introduction.
Cointegration tests.
Levels vs differences regression - motivation for cointegrated regression.
Leads and lags estimator for inference in cointegrated models (advanced).
Lagged independent variables.
Problem set 5 - an introduction to time series.
Mean and median lag.