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| Course Description |
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| Course Name |
: |
Limited Dependent Variables Models I |
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| Course Code |
: |
IEM 721 |
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| Course Type |
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Optional |
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| Level of Course |
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Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
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Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. SEDA ŞENGÜL
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| Learning Outcomes of the Course |
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Acquires the structure of data set for using limited dependent variable models.
Acquires and applies the regression models that are appropriate when the dependent variable is censored, truncated, binary, ordinal, nominal or count.
Applies the Limited Dependent variable models and interpret the results obtained.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
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None |
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| Recommended Optional Programme Components |
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None |
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| Aim(s) of Course |
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The aim of this course is to teach the regression models that are appropriate when the dependent variable is binary, ordinal, nominal , count, censored and truncated, to apply the regression models that are appropriate when the dependent variable is censored, truncated, binary, ordinal, nominal or count and to interpret the result obtained. |
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| Course Contents |
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The structure of data set for using Limited Dependent variable models,the advantage of Limited Dependent variable models, Linear Probability models, Binary Probit and Binary Logit models, Ordered Probit and Ordered Logit models, Sequantial Probit and Sequential Logit models, Multinomial Probit and Multinominal Logit models, Bivariate Probit and Bivariate logit models. |
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| Language of Instruction |
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Turkish |
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| Work Place |
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Class |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Data structure in Limited Dependent Variable Models |
Reading related sources |
Lecture |
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2 |
Limited Dependent Variable Models and OLS |
Reading related sources |
Lecture |
|
3 |
Linear probability model, Binary Probit and Binary Logit |
Reading related sources |
Lecture |
|
4 |
Linear probability model, Binary Probit and Binary Logit |
Reading related sources and preparing the data set |
Lecture and application session |
|
5 |
Linear probability model, Binary Probit and Binary Logit |
Reading related sources, problem set and application |
Lecture and application session |
|
6 |
Ordered Probit and ordered Logit Models |
Reading related sources |
Lecture |
|
7 |
Ordered Probit and ordered Logit Models |
Reading related sources, problem set and application |
Lecture and application session |
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8 |
Midterm exam |
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|
9 |
Multinominal Probit model and Multinominal Logit Model |
Reading related sources , |
Lecture |
|
10 |
Multinominal Probit model and Multinominal Logit Model |
Reading related sources, problem set and application |
Lecture and application session |
|
11 |
Sequantial Probit and Sequential Logit Models |
Reading related sources |
Lecture |
|
12 |
Sequantial Probit and Sequential Logit Models |
Reading related sources, problem set and application |
Lecture and application session |
|
13 |
Bivariate Probit and Bivariate Logit Models |
Reading related sources |
Lecture |
|
14 |
Bivariate Probit and Bivariate Logit Models |
Reading related sources, problem set and application |
Lecture and application session |
|
15 |
Conditional Probit and Conditional Logit Models |
Reading , problem set and application |
Lecture and application session |
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16/17 |
Final Exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Maddala, G.S (1988), Limited Dependent and Qualitative Variables in Econometrics
Cameron C.A., Trivedi P, K, (2005). Microeconometrics Methods and Applications. Cambridge University Press.
. Scott Long, Regression Models for Categorical and Limited Dependent Variables, 1997, Sage Publications;
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| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
75 |
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Homeworks/Projects/Others |
6 |
25 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Explains Econometric concepts |
4 |
|
2 |
Equipped with the foundations of Economics, develops Economic models |
5 |
|
3 |
Models problems using the knowledge of Mathematics, Statistics, and Econometrics |
4 |
|
4 |
Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems |
5 |
|
5 |
Collects, edits, and analyzes data |
4 |
|
6 |
Uses advanced software packages concerning Econometrics, Statistics, and Operation Research |
5 |
|
7 |
Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations |
5 |
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8 |
Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. |
5 |
|
9 |
Questions traditional approaches and their implementation and develops alternative study programs when required |
2 |
|
10 |
Recognizes and implements social, scientific, and professional ethic values |
4 |
|
11 |
Gives a consistent estimate for the model and analyzes and interprets its results |
4 |
|
12 |
Takes responsibility individually and/or as a member of a team; leads a team and works effectively |
2 |
|
13 |
Defines the concepts of statistics, operations research and mathematics. |
3 |
|
14 |
Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously |
0 |
|
15 |
Follows the current issues, and interprets the data about economic and social events. |
3 |
|
16 |
Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently |
3 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
4 |
56 |
| Assesment Related Works |
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Homeworks, Projects, Others |
6 |
5 |
30 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
8 |
8 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 146 |
| Total Workload / 25 (h): | 5.84 |
| ECTS Credit: | 6 |
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