Lecture Materials
Module I: Describing Data
- Lecture 1: Introduction; Summation Notation [slides, handout (filled)]
- Lecture 2: Distribution, Mean, Median, Percentiles [slides, handout (filled)]
- Lecture 3: Variance, Standard Deviation, Z-Score [slides, handout (filled)]
- Lecture 4: Covariance and Correlation [slides, handout (filled)]
- Lecture 5: Research Questions and Data [slides]
Additional Reading: NYT Article 2016 Elections, Stock & Watson Chapter 1
Module II: Coding in R
Here are the instructions for installing R and R Studio. You can find the datasets used in class in the Dropbox folder: Econ340 Datasets.
Module III: Random Variables
Module IV: Sampling and Estimation
Module V: Linear Regression
Notes: Lectures 15-17, Lecture 18 I & II, Lecture 19, Lecture 20
- Lecture 15: Ordinary Least Squares (OLS); R-Squared [slides, handout (filled)]
- Lecture 16: Prediction vs. Causal Inference [slides]
- Lecture 17: Inference (p-values, t-stats, confidence intervals) [slides, handout (filled)]
- Lecture 18: Omitted Variable Bias; Multiple Regression [slides, handout]
- Lecture 19: Categorical Variables; Interaction Terms [slides, handout (filled)]
- Lecture 20: Quadratic and Log Functional Forms [slides, handout (filled)]
- Lecture 21: Regression Analysis in R [slides]
- Lecture 22: Regression Analysis in R [slides]
Module VI: Advanced Topics
- Lecture 23: Experiments and Quasi-Experimental Methods [slides]
- Lecture 24: Differences-in-Differences [slides]
- Lecture 25: Big Data and Machine Learning [slides]
Additional Reading: 2021 Nobel Prize in Economics, Trade War