Panel Data Analysis

Course Objective

To provide a strong foundation in Panel Data analysis and modeling. It covers a thorough discussion of the panel data models and their demonstration using industry based data sets.

Learning Outcomes

Upon completion of this course, students will be able to

Extract useful information from large and complex data sets

Recognize patterns and trends in the data bases and model them.

Use appropriate modeling tools for panel data.

Detailed Syllabus

  1. Introduction to Panel Data-Characteristics, advantages and difficulties
  2. Error component models-One way- Two way
  3. Test of Hypotheses with Panel Data- Tests for Poolability of the Data -Tests for Individual and Time Effects -Hausman’s Specification Test
  4. Heteroskedasticity and Serial Correlation in the Error Component Model, Seemingly Unrelated Regressions with Error Component
  5. Simultaneous Equations with Error Components
  6. Dynamic Panel Data Models
  7. Unbalanced Panel Data Models
  8. Limited Dependent Variables and Panel Data
  9. Non-stationary Panels
  10. Spacial Panels