Instrumental Variables: Addressing Measurement Error and Reverse Causality

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Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Linear Regression: Understanding Quantitative Relationships

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Heteroskedasticity and Autocorrelation

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Linear Regression: Statistical Inference and Regularization

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Linear Regression: Maximum Likelihood Approach

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Probabilistic Models for Linear Regression

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Linear Regression: Estimation and Testing

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Instrumental Variables: Part 1

Introduces instrumental variables to address endogeneity issues, using examples to illustrate practical applications and testing requirements.