Responsible: Maria Grydaki
| SCHOOL | School of Management and Economics | ||
| ACADEMIC UNIT | Department of Accounting and Finance | ||
| LEVEL OF STUDIES | Undergraduate | ||
| COURSE CODE | 0803.4.006.0 | SEMESTER | 2nd |
| COURSE TITLE | Applied Econometrics ΙΙ | ||
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INDEPENDENT TEACHING ACTIVITIES if credits are awarded for separate components of the course |
WEEKLY TEACHING HOURS |
CREDITS |
| 3 | 5 | |
| Total | 3 | 5 |
| COURSE TYPE general background, special background, specialised general knowledge, skills development |
Mandatory, General Background |
| PREREQUISITE COURSES | None |
| LANGUAGE OF INSTRUCTION and EXAMINATIONS | English |
| OFFERED TO ERASMUS STUDENTS | Yes (in English) — Both Winter and Spring Semesters |
| COURSE WEBSITE (URL) | https://eclass.hmu.gr/courses/ACCFIN149/ |
On completion of this module, students should be able to:
MLO1
Critically investigate the basic assumptions of simple and multiple regression models and explore potential violations and their consequences for misinterpretation.
MLO2
Conduct multiple linear regressions using EVIEWS software using cross sectional and time series data and synthesise the results with economic theory.
MLO3
Use appropriate econometric methods to determine whether important relationships are independent of time, and hence be able to critically evaluate the data generation process.
Upon completion of this course, students should be able to conduct basic econometric analyses.
• Decision-making
• Researching, analyzing, and synthesizing data and information, using the necessary technologies
• Working independently
• Adapting to new situations
• Teamwork
• Working in an international environment
• Promoting free, creative, and inductive thinking
• Generating new research ideas.
Indicatively, but not limited to, the course presentation includes the following sections:
1. The Problem of Model Specification Error – Nonlinear Models and Variable Transformations
2. Multiple Regression Analysis with Categorical Data: Binary or Categorical Variables (or Dummy Variables)
3. Heteroscedasticity
4. Variance Modeling (ARCH-GARCH)
5. Basic Regression Analysis with Time Series Data
6. Autocorrelation - - Random Walk - ARMA(p,q) Autoregressive Models
7. Further Issues (Stationarity) in Time Series Analysis Using Least Squares
| DELIVERY Face-to-face, Distance learning, etc. |
Face to face | ||||||||||||
| USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY Use of ICT in teaching, laboratory education, communication with students |
• Use of ICT in teaching (PowerPoint presentations). • Uploading slides and course materials to the e-class platform. • Communicating with students via the e-class platform and email. • Using a projector • Using econometric software |
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| TEACHING METHODS The manner and methods of teaching are described in detail. |
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| STUDENT PERFORMANCE EVALUATION Description of the evaluation procedure |
• Assessment language: English. • Midterm written exam (30% or 40% of the total grade) • Students will be assessed through a final written exam, which may include: 1. Short-answer questions. 2. Problem-solving questions. • Students will have access to their assessment results to review explanations regarding their errors. • A special exam is provided for students with disabilities. |
The core text book for this module is:
Gujarati D. (2009), Basic Econometrics, 5th edition, McGraw-Hill Irwin (ISBN: 9780071276252).
Additional readings:
Hill, R. Carter, William E. Griffiths and Guay C. Lim (2018), Principles of Econometrics, 5th edition, John Wiley & Sons Pte Ltd.
Wooldridge, M. J. (2013), Introductory Econometrics: A Modern Approach, 5th edition, South-Western, Cengage Learning