• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. grasp basic econometric terminology and estimation and test principles for efficient inference with panel data

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Economics in a specialized way
   2. Pick out and validate problems relevant to his/her field,
   3. Develop an awareness of continuous learning in relation with modern technology
   4. Find out new methods to improve his/her knowledge.
   5. Effectively express his/her research ideas and findings both orally and in writing
   6. Be aware of issues relating to the rights of other researchers and of research subjects e.g. confidentiality, attribution, copyright, ethics, malpractice, ownership of data.

   Method of assessment

   1. Written exam
   2. Homework assignment
2. deal with estimation biases following from heterogeneity in individual characteristics and individual behaviour

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Economics in a specialized way

   Method of assessment

   1. Written exam
   2. Homework assignment
3. formulate static and dynamic econometric models for panel data on the basis of economic theories and to translate models for cross-section data and for time-series data into panel data models.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Economics in a specialized way
   2. Pick out and validate problems relevant to his/her field,
   3. Understand relevant research methodologies and techniques and their appropriate application within his/her research field,

   Method of assessment

   1. Written exam
   2. Homework assignment
4. estimate parameters in panel data models from actual observations and testing actual hypotheses by using suitable software

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Economics in a specialized way
   2. Pick out and validate problems relevant to his/her field,
   3. Understand relevant research methodologies and techniques and their appropriate application within his/her research field,
   4. Develop an awareness of continuous learning in relation with modern technology
   5. Find out new methods to improve his/her knowledge.
   6. Defend research outcomes at seminars and conferences.
   7. Write progress reports clearly on the basis of published documents, thesis, etc

   Method of assessment

   1. Written exam
   2. Homework assignment
5. read and understand project reports and journal articles that make use of the concepts and methods that are introduced in the course

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Economics in a specialized way
   2. Pick out and validate problems relevant to his/her field,
   3. Understand relevant research methodologies and techniques and their appropriate application within his/her research field,
   4. Develop an awareness of continuous learning in relation with modern technology
   5. Find out new methods to improve his/her knowledge.
   6. Summarize, document, report and reflect on progress,
   7. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Introduction to Econometrics; Introduction to the course
Week 2:	Statistical Models: Estimation and Testing; The linear model
Week 3:	Models with Individual Effects
Week 4:	Fixed Effects and Hierarchical Models
Week 5:	Random Effects Models
Week 6:	Random Effects Model: Maximum Likelihood Estimation. Panel Data Structures
Week 7:	Instrumental Variables; The Hausman-Taylor Estimator
Week 8:	Instrumental Variables; GMM Estimation Midterm Exam
Week 9:	Dynamic Models, Time Series, Panels and Nonstationary Data
Week 10:	Heterogeneous Parameter Models (Fixed and Random Effects), Two Step Analysis of Panel Data Models
Week 11:	Random Parameters, Discrete Random Parameter Variation, Continuous Parameter Variation
Week 12:	Nonlinear Models and Nonlinear Optimization; ML Estimation, M Estimation, GMM Estimation
Week 13:	Classical Estimation of Nonlinear Effects Models; Random and Fixed Effects Binary Choice Models
Week 14:	Sample Sample Selection Models and Models of Attrition
Week 15*:
Week 16*:	Final
Textbooks and materials:	1- Badi H. Baltagi, Econometric Analysis of Panel Data, Fifth Edition, Wiley, 2013. 2- Jeffrey M Wooldridge, Econometric Analysis of Cross Section and Panel Data, Second Edition, 2010, MIT Press 3- I Gusti Ngurah Agung, Panel Data Analysis using Eviews,
Recommended readings:	1- Badi H. Baltagi, Econometric Analysis of Panel Data, Fifth Edition, Wiley, 2013. 2- Jeffrey M Wooldridge, Econometric Analysis of Cross Section and Panel Data, Second Edition, 2010, MIT Press
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	30
Other in-term studies:		0
Project:		0
Homework:	3,4,5,9,10,14	20
Quiz:		0
Final exam:	16	50
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	6	14
Practice, Recitation:	0	0
Homework:	6	5
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	10	1
Mid-term:	2	1
Personal studies for final exam:	15	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)