Course website for our introductory applied econometrics class.
This class develops the theory and practice of regression analysis for economic data, with an emphasis on hands‑on applications in environmental, resource and international development economics. You will learn how to formulate hypotheses, design credible empirical strategies and interpret the results of econometric models. After introducing simple and multiple regression, we delve into issues such as omitted variable bias, heteroskedasticity, serial correlation and specification problems. The course then extends these tools to time‑series and panel data, qualitative response models, instrumental variables and causal designs including difference‑in‑differences, synthetic controls and regression discontinuity. In the final week we explore how machine learning and artificial intelligence can be integrated into econometric frameworks. Throughout the semester, examples are implemented in Python using real‑world datasets.
Instructor: Tamer Çetin — tamercetin@berkeley.edu
Lecture: Monday, Wednesday & Friday 2:00–2:59 pm, Genetics & Plant Bio 100
Office Hours: Fridays 3:00–5:00 pm (Giannini 238)
Course Site: Course materials, announcements and submissions will be hosted on bCourses.
By the end of the term you will be able to:
The tentative schedule below lists topics and readings (Wooldridge chapter numbers in parentheses). Dates and topics may adjust slightly as the semester progresses; any changes will be announced on bCourses.
| Week | Topic | Reading |
|---|---|---|
| 1 | Nature of Econometrics; Economic Data; Review of Probability/Statistics | Ch. 1–2 |
| 2 | Simple Regression Model; Omitted Variable Bias | Ch. 2–3 |
| 3 | Multiple Regression; Inference in Multiple Regression | Ch. 4 |
| 4 | Functional Form; Qualitative Information in Regression | Ch. 6–7 |
| 5 | Heteroskedasticity; Specification and Data Problems | Ch. 8–9 |
| 6 | Time Series Data; Serial Correlation | Ch. 10–12 (selected) |
| 7 | Pooled Cross Sections; Panel Data; Midterm Exam | Ch. 13 |
| 8 | Fixed Effects and Random Effects Models | Ch. 14 |
| 9 | Endogeneity and IV Estimation | Ch. 15 |
| 10 | Simultaneous Equations Models | Ch. 16 |
| 11 | Limited Dependent Variable Models (Logit/Probit) | Ch. 17 |
| 12 | Advanced Panel Data Topics | Ch. 14 (advanced) |
| 13 | Review & Special Topics in Causal Inference (DiD, RD) | Ch. 13–15 (applications) |
| 14 | Introduction to ML/AI in Causal Inference | Provided notes/articles |
Late Policy: assignments are penalized by 10 percentage points per 24 hours (maximum 72 hours). Conceptual discussion with classmates is encouraged, but all submitted work must be written and coded individually. AI tools may be used for brainstorming or scaffolding with explicit citation; students are responsible for correctness and originality.
Required Text: Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach. Any edition from the 5th onward is acceptable, as the core content and end‑of‑chapter problems are similar. The slides summarise key ideas, but the textbook provides fuller explanations, derivations and practice exercises that are essential for mastering the material.
Supplementary: Selected papers and handouts will be posted on bCourses.
Software: We use Python 3 (via Anaconda) with packages such as numpy, pandas, statsmodels, linearmodels and matplotlib. Students should install Anaconda and ensure their environment is working prior to the first coding lab.
Full Syllabus (PDF): syllabus_IAE_Fall25.pdf
The course follows the textbook Introductory Econometrics: A Modern Approach (8th edition) by Jeffrey Wooldridge. Each lecture roughly corresponds to a chapter of the book. A brief outline of topics is provided below.
Below is a list of lecture slides for each chapter. The files are provided in PowerPoint format (.pptx) for download. Some browsers will prompt you to save the file; others may open them directly if you have the appropriate viewer installed.
| Lecture | Topic | Slides |
|---|---|---|
| 1 | The Nature of Econometrics and Economic Data | CH 1 slides |
| 2 | The Simple Regression Model | CH 2 slides |
| 3 | Multiple Regression Analysis: Estimation | CH 3 slides |
| 4 | Multiple Regression Analysis: Inference | CH 4 slides |
| 5 | Multiple Regression Analysis: OLS Asymptotics | CH 5 slides |
| 6 | Multiple Regression: Further Issues | CH 6 slides |
| 7 | Multiple Regression Analysis with Qualitative Information | CH 7 slides |
| 8 | Heteroskedasticity | CH 8 slides |
| 9 | More on Specification and Data Issues | CH 9 slides |
| 10 | Basic Regression Analysis with Time Series Data | CH 10 slides |
| 11 | Further Issues in Using OLS with Time Series Data | CH 11 slides |
| 12 | Serial Correlation and Heteroskedasticity in Time Series Regressions | CH 12 slides |
| 13 | Pooling Cross Sections Across Time: Simple Panel Data Methods | CH 13 slides |
| 14 | Advanced Panel Data Methods | CH 14 slides |
| 15 | Instrumental Variables Estimation and Two‑Stage Least Squares | CH 15 slides |
| 16 | Simultaneous Equations Models | CH 16 slides |
| 17 | Limited Dependent Variable Models and Sample Selection | CH 17 slides |
| 18 | Advanced Time Series Topics | CH 18 slides |
| 19 | Carrying Out an Empirical Project | CH 19 slides |
| 20 | Course Review & Recap | CH 20 slides |