| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | İKT6219.01 | EKONOMETRİ II | Elective | 1 | 2 | 7 |
|
| Level of Course Unit |
| Second Cycle |
| Objectives of the Course |
| The purpose of this course is to have students learn some basic theoretical and practical knowledge regarding the econometrics science at an introductory level. |
| Name of Lecturer(s) |
| Dr. Öğr. Üyesi Fatih ÇİFTCİ |
| Learning Outcomes |
| 1 | Comprehend how to report and evaluate the results of the model with respect to simple (2-variable) regression analysis. | | 2 | Identify 2-variable regression model which is through the origin and know how to compute the coefficient of determination for this type of models. | | 3 | Understand what the scaling, units of measurement and standardization of the variables amount to in regression analysis. | | 4 | Know and interpret simple regression models in detail which are nonlinear in variables while linear in parameters. | | 5 | Have the knowledge pertaining to the issues of estimation and statistical inference for multiple regression models. | | 6 | Gain some experience about some applications of simple and multiple regression models. |
|
| Mode of Delivery |
| Formal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| - A general summary of the course Introduction to Econometrics I,
- Prediction for simple (2-variable) regression models, reporting and evaluating the findings of regression,
- Regression model that has no constant term (regression through the origin),
- Units of measurement and scaling of the variables, regression analysis with standardized variables,
- Regression models that are non-linear in variables (and linear in parameters), and interpretation of them,
- The analysis of multiple regression model: The problem of estimation,
- Introduction to the issue of statistical inference for multiple regression analysis,
- Some applications for simple and multiple regression analyses. |
| Weekly Detailed Course Contents |
|
| 1 | A general summary for the subject of analysis and estimation of simple (2-variable) regression model | Lecturing | | | 2 | A general summary for the subjects of confidence interval and hypothesis testing for simple regression model, the comparison of regression analysis and analysis of variance | Lecturing | | | 3 | Prediction of the values that the dependent variable would have, reporting the findings of the regression analysis and evaluation of them | Lecturing | | | 4 | Extensions of simple linear regression model (1): Regression through the origin and determination coefficient in this strand of models, scaling and unit of measurement, regression analysis with standardized variables | Lecturing | | | 5 | Extensions of simple linear regression model (2): Regression models with various functional forms | Lecturing | | | 6 | Extensions of simple linear regression model (3): Numerical examples for the estimation of regression models that are non-linear in variables and some suggestions for choosing correct functional form | Lecturing | | | 7 | Introduction to multiple regression models: Notation, assumptions and interpretation | Lecturing | | | 8 | Midterm exam | Examination | | | 9 | Regression parameters in multiple (3-variable) models and the ordinary least squares estimators of these parameters | Lecturing | | | 10 | The standard errors of ordinary least squares estimators in 3-variable regression models and some properties of these estimators, the coefficients of multiple determination and correlation | Lecturing | | | 11 | Introduction to the omitted variable bias, adjusted determination coefficient, an example for 3-variable regression model | Lecturing | | | 12 | Logarithmic-linear multiple regression model and its interpretation, polynomial regression models and some examples on them | Lecturing | | | 13 | Calculation and interpretation of partial correlation coefficients in 3-variable regression models, some applications for simple and multiple regression models | Lecturing | | | 14 | Normality assumption in multiple regression model, hypothesis testing for individual regression coefficients and its application, hypothesis testing for the overall regression model and its application | Lecturing | | | 15 | Final exam | Examination | |
|
| Recommended or Required Reading |
| Gujarati, D.N., Porter, D.C. (2012). Temel Ekonometri (5. Basımdan Çeviri: Şenesen, Ü., Şenesen, G.G.). İstanbul: Literatür Yayıncılık. (Temel Kaynak)
Wooldridge, J.M. (2013). Ekonometriye Giriş: Modern Yaklaşım (4. Basımdan Çeviri: Çağlayan, E., Ed.) (Cilt I). Ankara: Nobel Akademik Yayıncılık. |
| Planned Learning Activities and Teaching Methods |
|
| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | | Work Placement(s) | | None |
|
| Workload Calculation |
|
| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 1 | 1 |
| Attending Lectures | 14 | 3 | 42 |
| Problem Solving | 8 | 1 | 8 |
| Discussion | 7 | 1 | 7 |
| Question-Answer | 6 | 1 | 6 |
| Brain Storming | 6 | 1 | 6 |
| Self Study | 14 | 3 | 42 |
| Individual Study for Mid term Examination | 1 | 30 | 30 |
| Individual Study for Final Examination | 1 | 50 | 50 |
| Reading | 14 | 1 | 14 |
| Homework | 1 | 2 | 2 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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| Yozgat Bozok University, Yozgat / TURKEY • Tel (pbx): +90 354 217 86 01 • e-mail: uo@bozok.edu.tr |