COURSE INFORMATON
Course Title Code Semester L+P Hour Credits ECTS
Econometrics III * ECMZ   401 7 3 3 3

Prerequisites and co-requisites
Recommended Optional Programme Components None

Language of Instruction English
Course Level First Cycle Programmes (Bachelor's Degree)
Course Type
Course Coordinator Prof.Dr. Kenan LOPCU
Instructors
Prof.Dr.KENAN LOPCU1. Öğretim Grup:A
 
Assistants
Goals
The aim of this course is to provide students with the basic concepts of econometrics and to strengthen econometric background. This course also aims to equip students with skills to carry out independent applied research and be able to apply certain econometric methods to economic problems.
Content
This course, which is the continuation of econometrics I and econometrics II, will focus on topics such as simultaneous equations estimation methods, dynamic models, discrete choice models, limited dependent variable models, panel data analysis, time series analysis.

Learning Outcomes
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Course's Contribution To Program
NoProgram Learning OutcomesContribution
12345
1
Explains Econometric concepts
X
2
Acquires basic Mathematics, Statistics and Operation Research concepts
X
3
Equipped with the foundations of Economics, and develops Economic models
X
4
Describes the necessary concepts of Business
X
5
Models problems with Mathematics, Statistics, and Econometrics
X
6
Estimates the model consistently and analyzes & interprets its results
X
7
Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems
X
8
Collects, edits, and analyzes data
X
9
Uses a package program of Econometrics, Statistics, and Operation Research
X
10
Effectively works, take responsibility, and the leadership individually or as a member of a team
X
11
Awareness towards life-long learning and follow-up of the new information and knowledge in the field of study
X
12
Develops the ability of using different resources in the form of academic rules, synthesis the information gathered, and effective presentation in an area which has not been studied
X
13
Uses Turkish and at least one other foreign language, academically and in the business context
X
14
Good understanding, interpretation, efficient written and oral expression of the people involved
X
15
Improves his/herself constantly by defining educational requirements considering interests and talents in scientific, cultural, art and social fields besides career development
X
16
Questions traditional approaches and their implementation while developing alternative study programs when required
X
17
Recognizes and implements social, scientific, and professional ethic values
X
18
Follows actuality, and interprets the data about economic and social events
X

Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Model specification, model specifation error and types of model specification error Readings and problem set
2 Tests of model specification error , errors of measurement and model selection criteria. Readings and problem set
3 Dynamanic models: Estimation of Distributed -Lag models. The Koyck Approach, Adaptive expectation, Partial adjustment Readings and problem set
4 Estimation of Autoregressive models: Almon approach, polinominal spline distrubuted lags, exogeneity tests, the wu-hausman test Readings and problem set
5 Estimation of Autoregressive models: Almon approach, polinominal spline distrubuted lags, exogeneity tests, the wu-hausman test Readings and problem set
6 Simultaneous equation models:specifation and identification in Simultaneous equation models Readings and problem set
7 Maximum likelihood (ML): likelihood ratio test, wald test, lagrange multiplier tests Readings and problem set
8 Midterm
9 Maximum likelihood (ML): likelihood ratio test, wald test, lagrange multiplier tests Readings and problem set
10 Discrete choice models: Linear probability model, probit and logit models Readings and problem set
11 Limited dependent variables: truncating, censoring, and sample selection Readings and problem set
12 Tobit model, Heckman model Readings and problem set
13 Panel data analysis: general terms of panel data, advantages of panel data, pooled least squares Readings and problem set
14 panel data analysis fixed effects models and random effects models Readings and problem set
15 Time series modeling: ARMA processes, testing and estimation for stationarity, forecast Readings and problem set
16-17 Final Exam

Recommended or Required Reading
Textbook
Additional Resources