| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Analysis Of The Limited Dependent Variable | EKO 570 | 0 | 3 + 0 | 3 | 6 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | YUKSEK_LISANS |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Dr.Öğr.Üyesi SAYIN SAN |
| Dersi Verenler | Dr.Öğr.Üyesi SAYIN SAN, |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Temel Öğretim |
| Dersin Amacı | This course examines several types of advanced regression models that are frequently used in policy analysis and social science research. The key similarity of these models is that they involve dependent variables that violate one or more of the assumptions of the Ordinary Least Squares (OLS) regression model. |
| Dersin İçeriği | The main models examined in the course are binary logit and probit, multinomial logit, ordinal probit, tobit, and the family of Poisson regression models. All these models are estimated using maximum likelihood estimation (MLE). The Heckman correction for selection is also addressed. |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Analyze choice microdata using statistical packages | Lecture, Group Study, Case Study, | Testing, Homework, Project / Design, |
| 2 | Interpret the results of model estimation and prediction | Case Study, Group Study, Lecture, | Project / Design, Homework, Testing, Performance Task, |
| 3 | Summarize the empirical implications of the statistical assumptions of regression models with limited dependent variables | Group Study, Lecture, | Homework, Testing, |
| 4 | Formulate and test behavioural hypotheses | Case Study, Lecture, | Homework, Testing, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | From regression to conditional probability models | |
| 2 | Estimation and statistical tests | |
| 3 | Introduction to binary choice | |
| 4 | Maximum likelihood estimator | |
| 5 | Binary logit and probit models | |
| 6 | Probit models | |
| 7 | Mutinomial probit model | |
| 8 | MIDTERM | |
| 9 | Conditional and multinomial logit models | |
| 10 | Bayes estimator of the mixed logit model | |
| 11 | Discrete Outcomes | |
| 12 | Censored Outcomes | |
| 13 | Truncated Outcomes | |
| 14 | FINAL |
| Kaynaklar | |
|---|---|
| Ders Notu | Microeconometrics using STATA, (2009). Cameron, Colin A. and Pratin K. Trivedi, STATA Publication. Analysis of Micro Data, (2006). Winkellman, Rainer and Stefan Boes, Springer Publication. Regression Models for Categorical Dependent Variables using STATA, (2001). Long J. Scott, STATA Press |
| Ders Kaynakları | |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Economic and financial issues in conjunction with the expansion and deepening of information by econometric analysis information to evaluate, interpret and apply | X | |||||
| 2 | Using the theoretical knowledge in finance and economics combine the ability to apply scientific methods and knowledge of econometric methods to analyze and interpret | X | |||||
| 3 | Problems experienced in the financial sector to analyze with scientific methods and solutions development | X | |||||
| 4 | The oral and visual way to transfer to Current developments in the financial sector, for the financial sector and outside groups as written | X | |||||
| 5 | Both public as well as how it should be in the private sector require highly skilled personnel; have the theoretical knowledge and practical advanced analysis capabilities in economic and financial matters | X | |||||
| 6 | At least one foreign language spoken and written communication having the ability ("European Language Portfolio Global Scale", Level B2 | X | |||||
| 7 | they internalize the knowledge and problem-solving skills, ability to apply interdisciplinary studies | X | |||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ödev | 20 |
| 2. Ödev | 20 |
| 3. Ödev | 20 |
| 4. Ödev | 20 |
| 5. Ödev | 20 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 50 |
| 1. Final | 50 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 5 | 80 |
| Mid-terms | 1 | 3 | 3 |
| Assignment | 2 | 3 | 6 |
| Project / Design | 3 | 3 | 9 |
| Final examination | 1 | 4 | 4 |
| Toplam İş Yükü | 150 | ||
| Toplam İş Yükü / 25 (Saat) | 6 | ||
| Dersin AKTS Kredisi | 6 | ||