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Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (MASTER) (WITH THESIS) | |
| Code | IEM752 |
| Name | Bayesian Econometrics |
| Term | 2025-2026 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. ÇİLER SİGEZE GÜNEY |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
This course aims to teach students the theoretical foundations and applications of Bayesian econometric methods. It is aimed to learn Bayesian inference, model selection and estimation techniques as an alternative to traditional econometrics and to gain the ability to apply Bayesian models using statistical software programs such as STATA, R .
Course Content
The basic principles of Bayesian econometrics, its historical development and its comparison with the classical frequentist approach will be discussed. Bayesian inference methods (prior distributions, posterior analysis, MCMC simulations) in mathematical modelling of economic theories will be examined in detail, and estimation of simple and multiple regression models from a Bayesian perspective will be emphasized. In addition, extended applications such as panel data, nonlinear models will be covered.
Course Precondition
There is no prerequisites.
Resources
Gary, K. (2003). Bayesian econometrics
Notes
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. Chapman and Hall/CRC.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the basic concepts of Bayesian statistics (prior distribution, posterior distribution, Markov Chain Monte Carlo). |
| LO02 | Distinguish the differences in the analysis of classical econometrics and Bayesian econometrics. |
| LO03 | Estimates linear and non-linear econometric models with Bayesian approach. |
| LO04 | Uses Bayes factor and other criteria for model comparison |
| LO05 | Apply Bayesian methods in panel data models. |
| LO06 | Makes Bayesian analysis with different computer programmes. |
| LO07 | Interpret the results of Bayesian analyses appropriately. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explains contemporary concepts about Econometrics, Statistics, and Operation Research | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences | 3 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field of research | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Searches for new approaches and methods to solve problems being faced | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 4 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Collects/analyzes data in a purposeful way | 4 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | 2 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 5 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 5 |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Frequentist and Bayesian Paradigm | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Bayes Theorem and Basic Concepts | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Markov Chain Monte Carlo (MCMC) and related algorithms | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Bayesian Linear Regression | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Model Comparison and Selection: Bayes factor, BIC, DIC criterias | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Hierarchical (Multilevel) Models : Fixed/random effects | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Hierarchical (Multilevel) Models: mixed models | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Preparing for the midterm exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Bayesian Panel Data Models: random effects model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Bayesian Panel Data Models: mixed effects model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Bayesian Dynamic panel data models | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım |
| 12 | Bayesian Nonlinear Models: Probit- Logit | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Bayesian Nonlinear Models: Tobit, Poisson | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Bayesian Nonlinear Models: Multinominal Logit model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Bayesian Nonlinear Models: Multinominal Probit model | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
| 17 | Term Exams | Final exam preparation | Ölçme Yöntemleri: Ödev, Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 20 | 40 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||