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Description of Individual Course Units| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | İKT6107.01 | PANEL VERİ EKONOMETRİSİ I | Elective | 1 | 1 | 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 | Identify the science of econometrics and comprehend that the econometrics science can be used as an instrument for testing economic predictions. | | 2 | Have some basic knowledge regarding single-equation simple (2-variable) regression models. | | 3 | Learn how to estimate the parameters of simple regression model by making use of the method of ordinary least squares. | | 4 | Get the knowledge about the statistical properties of classical linear regression model. | | 5 | Know the standard errors of ordinary least squares estimators and the goodness-of-fit of the model. | | 6 | Compute the values of coefficients, standard errors and goodness-of-fit for simple regression model. |
| | Mode of Delivery | | Uzaktan Eğitim | | Prerequisites and co-requisities | | None | | Recommended Optional Programme Components | | None | | Course Contents | | - The definition, content, objective and methodology of the econometrics science,
- The nature of regression analysis,
- Some practical information pertaining to single-equation simple (2-variable) regression models,
- Estimation of simple regression models: The method of ordinary least squares,
- The assumptions underlying the classical linear regression model and the Gauss-Markov theorem,
- Standard errors of the ordinary least squares estimators,
- The coefficient of determination (the goodness-of-fit): The methods of calculation and the meaning,
- An application for estimating the parameters, their standard errors, and the coefficient of goodness-of-fit (determination) of simple regression model,
- Probability distribution of error term,
- Interval estimation and hypothesis testing for simple regression model. | | Weekly Detailed Course Contents | |
| 1 | The definiton and content of econometrics, the cause for econometrics science to emerge as a separate field of science, steps of an econometric analysis | Lecturing | | | 2 | The nature of regression analysis (1): The origin of the term regression and the modern interpretation of it, statistical relationship vs. deterministic relationship, regression vs. causation, regression vs. correlation | Lecturing | | | 3 | The nature of regression analysis (2): The terminology and some notations for econometrics, the types of data construction, the measurement scales of variables | Lecturing | | | 4 | Introduction to the simple (2-variable) regression model: A hypothetical example, population and sample regression functions, linearity in parameters and in variables, the causes for the error term to be present in the regression model | Lecturing | | | 5 | Estimation of the simple regression model (1): The method of ordinary least squares | Lecturing | | | 6 | Estimation of the simple regression model (2): The assumptions of classical linear regression model | Lecturing | | | 7 | Estimation of the simple regression model (3): The standard errors of ordinary least squares estimators, the Gauss-Markov theorem | Lecturing | | | 8 | Midterm exam | Examination | | | 9 | The goodness-of-fit (the determination) coefficient: The methods of calculation and the meaning for it with respect to regression model | Lecturing | | | 10 | A numerical example for estimating the values of the coefficients, the standard errors, and the determination coefficient of simple regression model | Lecturing | | | 11 | The Gauss-Markov theorem and normal distribution of the error term: The classical normal linear regression model | Lecturing | | | 12 | Interval estimation and hypothesis testing for simple regression model (1): Basic knowledge regarding interval estimation, confidence intervals for the constant and slope parameters | Lecturing | | | 13 | Interval estimation and hypothesis testing for simple regression model (2): Confidence interval for the error variance, hypothesis testing through the confidence interval approach | Lecturing | | | 14 | Interval estimation and hypothesis testing for simple regression model (3): Hypothesis testing through the test of significance approach, some practical information on hypothesis testing | 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 | | Turkish | | 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 | 7 | 98 | | Individual Study for Mid term Examination | 1 | 9 | 9 | | Individual Study for Final Examination | 1 | 12 | 12 | | 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 |
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