17 AI Video Lectures

Short Video Overviews

AI-generated video lectures for every chapter. Watch them here or on YouTube.

Watch on YouTube
1

Part I: Statistical Foundations

Chapter 1: Analysis of Economics Data

Regression basics, scatter plots, OLS, R-squared, association vs. causation

Chapter 2: Univariate Data Summary

Histograms, box plots, summary statistics, log transformations, z-scores

Chapter 3: The Sample Mean

Random variables, sampling distributions, CLT, standard error, Monte Carlo

Chapter 4: Statistical Inference for the Mean

t-distribution, confidence intervals, hypothesis testing, significance

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Part II: Bivariate Regression

Chapter 5: Bivariate Data Summary

Correlation, OLS estimation, R-squared, regression asymmetry, LOWESS

Chapter 6: The Least Squares Estimator

Population vs sample models, unbiasedness, Gauss-Markov theorem

Chapter 7: Statistical Inference for Bivariate Regression

t-statistics, confidence intervals for slopes, robust standard errors

Chapter 8: Case Studies for Bivariate Regression

OECD health economics, CAPM stock betas, Okun's Law, outliers

Chapter 9: Models with Natural Logarithms

Log approximation, semi-elasticity, elasticity, exponential growth, Rule of 72

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Part III: Multiple Regression

Chapter 10: Data Summary for Multiple Regression

Partial effects, correlation matrices, FWL theorem, VIF multicollinearity

Chapter 11: Statistical Inference for Multiple Regression

Confidence intervals, t-tests, joint F-tests, ANOVA model comparison

Chapter 12: Further Topics in Multiple Regression

HC1 and HAC standard errors, prediction intervals, power curves, bootstrap

Chapter 13: Case Studies for Multiple Regression

Cobb-Douglas, Phillips curve, RCT, diff-in-diff, RDD, instrumental variables

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Part IV: Advanced Topics

Chapter 14: Regression with Indicator Variables

Dummy variables, interaction terms, Chow test, ANOVA, dummy variable trap

Chapter 15: Regression with Transformed Variables

Log specifications, quadratic models, standardized coefficients, retransformation bias

Chapter 16: Checking the Model and Data

VIF, heteroskedasticity, autocorrelation, omitted variable bias, diagnostics

Chapter 17: Panel Data, Time Series Data, Causation

Fixed effects, first differencing, ACF, HAC, ADL models, causality