COURSE UNIT TITLE

: MICRO ECONOMETRICS I

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EKO 6103 MICRO ECONOMETRICS I ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM KIREN GÜRLER

Offered to

Econometrics

Course Objective

Be able to evaluate and make interpretation of econometric cross-sectional and pooled data.

Learning Outcomes of the Course Unit

1   1- To explain the basic concepts used in micro econometrics,
2   2- To explain cross-section data and pooled data,
3   3- Build models to explain the difference between categorical and scaled data,
4   4- To introduce the difference between sequential and nested models with models,
5   5- Using models with count data,
6   6- Working with limited dependent variable models and regime models with truncated and censored data,
7   7- To use cross-section data in policy analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1- Nonlinear Regression Models and Maximum Likelihood Estimator Two-State Models-Linear Probability, Logit and Probit Model-
2 2- Linear Instrument Variable Regression Model
3 3- Multinonial Models: Multinomial Logit and Probit Models
4 4- Ordered Models: Ordered Logit, Ordered Probit and Generalized Ordered Logit Models
5 5- Sequential Logit Model
6 6- Nested Logit Model
7 7- Count Data Models: Poisson, Negative Binomial, Zero-Counting Poisson and Zero- Counting Negative Binomial Regression Models
8 8- Models with Limited Dependent Variables: Truncated and Censored Data, Trimmed Regression Model
9 9- Tobit Model, Tobit and Probit Model Relationship Heckman Selection Model
10 10- Switching Regression Model The Concept of Regime, External and Intrinsic Switching Regression Models
11 11- Micro Econometric Methods in Policy Analysis
12 12- Difference of Differences Method
13 13- Regression Discontinuity Method
14 14- Tendency Score Matching Method

Recomended or Required Reading

Seda Şengül-Şenay Üçdoğruk Birecikli /Editörler), Uygulamalarla Mikro Ekonometri, Nobel Yayınevi 2021.
Cameron, A. C. ve Trivedi, P. K. (2005). Microeconometrics: Methods and Applications. New York: Cambridge University Press.
Cameron, A. C. ve Trivedi, P. K. (2009). Microeconometrics Using Stata. Vol. 5. College Station, TX: Stata Press.
Long, J. S. ve Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata. USA: Stata press. Third Edition.
Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. USA: SAGE Publications.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations, Econometric package programs, Stata program

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.20 + FIN* 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + RST* 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
TOTAL WORKLOAD (hours) 0

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.1222123222
LO.2121322321
LO.3223213221
LO.4231112221
LO.5211222222
LO.6222111222
LO.7222222222

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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