COURSE UNIT TITLE

: PANEL DATA ECONOMETRICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ECN 5038 PANEL DATA ECONOMETRICS ELECTIVE 3 0 0 6

Offered By

Economics (English)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR ERDOST TORUN

Offered to

Economics (English)

Course Objective

This is a course on applied econometrics dealing with 'panel' or 'longitudinal' data sets. Topics to be studied include specification, estimation, and inference in the context of models that include individual (firm, person, etc.) and/or time effects. We will begin with a development of the standard linear regression model, then apply it to panel data settings involving 'fixed' and 'random' effects. The basic model will be extended to dynamic models with recently developed GMM and instrumental variables methods. We will consider numerous applications from the literature, including static and dynamic panel data regression models. The use of computer is an integrated part of the course. No prior knowledge of programming is required. Students are expected to prepare a term project to demonstrate their skills developed in the course.

Learning Outcomes of the Course Unit

1   Be able to collect raw data related to micro economic topics, and make them ready for statistical and econometric analysis.
2   Demonstrate understanding of building econometric models that describe the data generating process behind data.
3   Identify problems with existing econometric models so that the learner could employ appropriate econometric tools to solve the problem.
4   Be able to interpret the estimation results so that the learner can draw implications from the results.
5   Demonstrate engaging an independent empirical research in order to prepare a tem project

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Math and Statistics Review
2 The one-way error component regression model
3 The two-way error component regression model
4 Heteroscedasticity and serial correlation in the error component model
5 Seemingly unrelated regressions with error components
6 Unbalanced panel data models
7 Midterm
8 Unbalanced panel data models
9 Unbalanced panel data models
10 Dynamic panel data models
11 Dynamic panel data models
12 Dynamic panel data models
13 Limited dependent variables and panel data
14 General Overview

Recomended or Required Reading

1. Badi H. Baltagi Econometrics Analysis of Panel Data 2nd Ed. Wiley
2. Lecture Notes

Planned Learning Activities and Teaching Methods

1. Lectures
2. Class Discussions
3. Term Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FCG FINAL COURSE GRADE
3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. The learner will use necessary statistical and econometric tools to engage independent research.
2. The learner will clearly recognize the problems with existing econometric models.
3. The learner will build econometric models for estimation purposes.
4. The learner will interpret empirical results.
5. The learner will draw some policy implications from estimation results.

Language of Instruction

English

Course Policies and Rules

It is obligatory to attend at least 70% of the classes.

Contact Details for the Lecturer(s)

adnan.kasman@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Tutorials 13 1 13
Preparing assignments 1 50 50
Preparation for midterm exam 1 10 10
Preparation for final exam 1 20 20
Preparations before/after weekly lectures 13 1 13
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 149

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.135
LO.24
LO.3525
LO.435
LO.5545