sap course 1429293835

Course Code & Number:

ECON 434

Course Title:

Panel Data Analysis

Level of Course:



(3+0+0) 3 TEDU Credits, 6 ECTS Credits

Catalog Description:

Applied econometrics dealing with 'panel' or 'longitudinal' data sets. Specification, estimation, and inference in the context of models that include individual (firm, person, etc.) and/or time effects. Panel data estimators, instrumental variables estimators, and maximum likelihood estimation of limited dependent variable models.

Pre-requisites & Co-requisites:

Pre-requisites: ECON 332_O
Co-requisites: NONE
Year of Study: 
Mode of Delivery: 
Language of Instruction: 
Course Type: 
Required Reading: 
1. Wooldridge. J., Econometric Analysis of Cross-Section and Panel Data. MIT Press, Boston, 2002.
Course Objective: 

This course presents an overview of a selection of key topics in panel data econometrics, a branch dealing with data structures which have both time and group dimensions. Although the emphasis of the course is on practical implementation of the techniques to be learned, a sound knowledge of statistics, basic econometrics and matrix algebra will help make the most of the course material. The techniques to be learned during the course will be demonstrated using STATA statistical software.

Extended Description: 

Why Use Panel Data? The N and T Dimensions. Static Panel Data. Fixed effects. Random effects. Fixed or random effects?. Hausman Test. Fixed and Random Effects Reconciled: The Mundlak and Hausman-Taylor Estimators. Hypothesis Testing with Panel Data. Testing for and Addressing Residual Heteroscedasticity. Testing for Serial Correlation.. Dynamic Panel Data. The Problem: Bias in the Estimates for Short T. An Overview of GMM estimators: Arellano and Bond, Arellano and Bover and Blundell and Bond System Estimator. The Logit Regressions on Panel Data. Count Data Regressions.

Computer Usage: 
Students will use Stata and MS Office applications (Word, Excel, Access, Powerpoint) to work on their weekly assignments about 4 hours a week.
Learning Outcomes: 

Upon succesful completion of this course, a student will be able to
1. Formulate static and dynamic econometric models for panel data on the basis of economic theories. 
2. Recognise why panel data is a richer data source than pure cross-section data, pure time-series data and repeated cross sections. 
3. Estimate parameters in panel data models by using suitable software. 
4. Communicate meaningfully and productively with others (including practitioners and professionals in the financial services industry) on time series analysis issues. 
5. Evaluate their own work and the literature in management that uses regression analysis 
6. Write an applied econometrics project and present the results to the class 
7. Gain a working knowledge of STATA 

Planned Learning Activities and Teaching Methods: 
Case Study/Scenarion Analysis
Guest Speakers
Web Searching
Assessment Methods and Criteria: 
Test / Exam
Presentation (Oral/Poster)

Student Workload:

Quizzes /Homeworks
Midterm Exam 1
Final Exam
Research Review

Prepared By:

Jülide Yıldırım Öcal

Revised By: