Lecture 1: Multiple Regression Analysis: Introduction, Slides

Lecture 2: Multiple Regression Analysis: Properties, Slides, (Empirical example - WAGE2, VOTE1, code) (Interpreting STATA output SRM, MRM)

Lecture 3: Multiple Regression Analysis: Properties, Slides, (Empirical example - WAGE2, code) 

Lecture 4: Multiple Regression Analysis: Properties, Slides

Lecture 5: Multiple Regression Analysis: Inference, Slides, (Empirical example - WAGE2, code)

Lecture 6: Multiple Regression Analysis: Inference, Slides, (F-distribution, code for FWL) 

Lecture 7: Multiple Regression Analysis: Matrix Approach, Slides, (Empirical example - WAGE2, code) (Optional material - matrix algebra)

Lecture 8: Multiple Regression Analysis: Matrix Approach, Slides

Lecture 9: Multiple Regression Analysis: WLS & GLS, Slides, (Optional material - asymptotics)

Lecture 10: Rubin's Causal Model, Slides, (Potential outcomes framework)

Lecture 11: Rubin's Causal Model, Slides, (Potential outcomes framework)

Lecture 12: Instrumental Variables, Slides

Lecture 13: Instrumental Variables, Slides, (Empirical example - Paper, CARD, code)

Lecture 14: Panel Data and Fixed Effects, Slides, (Panel Data)

Lecture 15: Panel Data and Fixed Effects, Slides, (Additional material - slides, data, code)

Lecture 16: Panel Data and First Differences, Slides, (Empirical example - data, code)

Lecture 17: Difference-in-Differences, Slides, (Card and Krueger - code)

Lecture 18: Difference-in-Differences, Slides, (DiD)

Lecture 19: Clustering and Standard Errors, Slides, (Empirical example - data, code)

Lecture 20: Clustering and Standard Errors, Slides, (Optional material)

Lecture 21: Regression Discontinuity Design, Slides, (Simulation example - code, Additional material - RDD)

Lecture 22: Regression Discontinuity Design, Slides, (RDD in STATA, data, code, manipulation, data, code)

Lecture 23: Class Discussion on Causal Methods

Lecture 24: Class Presentations

Lecture 1: Introduction - Lower Pollution, Longer Lives (A Polluted Mind)

Lecture 2: Introduction - Income and Pollution, World Development Indicators, (slides)

Lecture 3: Introduction - Economic Growth and Environment, (slides, data, code)

Lecture 4: Introduction - Confronting the Environmental Kuznets Curve, (slides)

Lecture 5: Air - Consequences of Air Pollution, (smog to crime, air pollution and violent crime, WEF, wind and pollution)

Lecture 6: Air - Consequences of Air Pollution, (inversions)

Lecture 7: Air - Consequences of Air Pollution, (IVs)

Lecture 8: Air - Group 1: Air Pollution and Health

Lecture 9: Air - Group 2: Air Pollution and Human Capital

Lecture 10: Space - Applications of Satellite Data, (Data resources - NASA, ISRO, SHRUG, CEDA, FAOSTAT, GAEZ, WBOD)

Lecture 11: Space - Applications of Satellite Data, (GIS for economists with R)

Lecture 12: Space - Applications of Satellite Data, (Spatial data with STATA, NASA-FIRMS)

Lecture 13: Fire - Incentives and Forest Fires (Wildfires and forest fires - WRI, NASA, EPA)

Lecture 14: Fire - Group 3: Forest Fires

Lecture 15: Fire - Wildfires, Fiscal Impacts and Government Spending

Lecture 16: Fire - Group 4: Crop Prices and Deforestation

Lecture 17: Earth - Natural Resources and Development, (slides) (Resource curse, Oil discoveries)

Lecture 18: Earth - Group 5: Oil and Resource Curse 

Lecture 19: Earth - Mining and Conflicts, (slides)

Lecture 20: Earth - Group 6: Mining and Impacts

Lecture 21: Water - Trade and Water (Trade and water scarcity, Water markets) (India - Water crisis, CGWB, Water export)

Lecture 22: Water - Group 7: Water Pollution and Impacts

Lecture 23: Water - Group 8: Agriculture, Water and Adaptation

Lecture 24: Discussion on Research Topics

Lecture 1: Introduction & Measures of Central Tendency, Slides, (Extra Material)

Lecture 2: Measures of Dispersion & Chebyshev’s Inequality, Slides, (Example)

Lecture 3: Normal Datasets & Correlation, Slides, (Correlation)

Lecture 4: Counting Principles, Slides, (Examples)

Lecture 5: Sample Space & Events, Slides, (Sample Spaces)

Lecture 6: Probability, Slides, (Conditional Probability, Multiplication Rule)

Lecture 7: Bayes' Theorem, Slides, (Bayes' Rule, Examples)

Lecture 8: Random Variables, Slides, (Examples)

Lecture 9: Expectation and its Properties, Slides, (Joint PDF and CDF)

Lecture 10: Covariance and Some Important Results, Slides, (WLLM, simulation)

Lecture 11: Discrete Distirbutions, Slides, (Bernoulli and Binomial, Example)

Lecture 12: Discrete Distributions, Slides, (Example, Link between Binomial and Poisson, Taylor Series)

Lecture 13: Continuous Distributions, Slides, (Uniform, Exponential)

Lecture 14: Functions of One Random Variable, Slides, (Examples)

Lecture 15: Continuous Distributions, Slides, (Normal distribution, Normal to Standard Normal, Reading Normal Tables)

Lecture 16: Continuous Distributions, Slides, Simulation, (Simulating Observations)

Lecture 17: Sampling Distribution, Slides, Simulation, (t-distribution, CLT, Example)

Lecture 18: Sampling Distribution, Slides

Lecture 19: Point and Interval Estimation, Slides, (Notes, CI for means)

Lecture 20: Point and Interval Estimation, Slides, (t-score, CI for proportions)

Lecture 21: Point and Interval Estimation, Slides, (CI for diff in means)

Lecture 22: Hypothesis Testing, Slides, (Test of means, Test of proportions)

Lecture 23: Errors in Hypothesis Testing, Slides, (Errors, Power)

Lecture 24: T-test and Paired T-test, Slides, (t-test, Paired t-test)