 # Learn Stata

### Introduction to Panel Data Using Stata ## NetCourse®471: Introduction to Panel Data Using Stata

Course length: 6 weeks (4 lessons)

See dates.

## Enrol:

#### 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

Note: This course is platform independent. You can use Stata on Windows, Mac or Linux.

#### Trainer: Jamie Lai #### Trainer: Gustavo Sánchez #### Trainer: Chris Cheng #### Course content

Lecture 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
• Moment-based estimation
• Panel data, regression, and efficiency
• Closing remarks

Lecture 2

• Random-effects model
• The 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.

Lecture 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

Lecture 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