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Content:
Become an expert in the analysis and implementation of linear,
nonlinear, and dynamic panel-data estimators using Stata. This course
focuses on the interpretation of panel-data estimates and the
assumptions underlying the models that give rise to them. The course is
geared for researchers and practitioners in all fields. The breadth of
the lectures will be helpful if you want to learn about panel-data
analysis or if you are familiar with the subjects.
The concepts presented are reinforced with practical exercises at the
end of each section. We also provide additional exercises at the end of
each lecture and access to a discussion board on which you can post
questions for other students and the course leaders to answer.
Prerequisites:
- Stata 18 installed and working
- Course content of NetCourse 101 or equivalent knowledge
- Familiarity with basic time-series, cross-sectional summary statistics and linear regression
- Internet web browser, installed and working
(course is platform independent)
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Course content
Lesson 1
- An introduction to panel data and its features
- Getting started with panel data
- Summary statistics and dynamics
- Overview of basic concepts
- Data generation
- The regression model
- Variance–covariance estimators
- Margins and marginal effects
- Basic panel-data estimation concepts
- Panel data, regression, and efficiency
- Closing remarks
Lesson 2
- Random-effects model
- Fixed-effects model
- Within estimator
- Comparing within and random-effects estimates
- First-differenced estimator
- Deciding between random and fixed effects
- Hausman test
- Mundlak test
- Population-averaged models
Note:
There is a one-week break between the posting of Lessons 2 and 3;
however, course leaders are available for discussion.
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Lesson 3
- Probit model
- Probit models for panel data: Random effects
- Probit models for panel data: Population averaged
- Probit models for panel data: Remarks
- Logit model
- Logit models for panel data: Random effects
- Logit models for panel data: Fixed effects
- Logit models for panel data: Population averaged
- Poisson model
- Poisson models for panel data
Lesson 4
- Endogeneity
- Cross-sectional estimation under endogeneity
- Panel-data estimation under endogeneity
- Dynamic models
- Building your own dynamic models
- A more complex dynamic structure
- Concluding remarks
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