1. Learning Objectives – General learning outcomes

The main objective of the course is to acquire knowledge and skills on the basis of which students will be able to integrate theoretical approaches-theories with data and facts that are actually observed. Econometrics, like Statistics, are sciences that serve the above purposes, and this, of course, in direct association with the use of Information Technology and other advanced econometric software. The more complex the relationships and the evolution / change of socio-economic phenomena, the greater the need to develop effective and flexible tools for their analysis.

During the course, are presented and analyzed (a) the most important principles and concepts of econometrics, (b) the most useful models of econometrics as well as (c) the modern statistical and econometric methodology applied to the investigation, control and prediction of phenomena, in particular in conditions of uncertainty. Emphasis is given to: 1. empirical implementation of econometric models to socio-economic as well as spatial analysis; 2.the interpretation of results according to the possibilities and limitations of econometrics.

• Knowledge

At the end of the course, students are able to understand the statistical and econometric methods of analyzing socio-economic phenomena that also have a spatial dimension.

• Skills

At the end of the course, students are able to apply the statistical methods of data analysis as well as the main linear and non-linear models (multiple regression, panel data regression, logistic regression, etc.). At the same time, they have the ability to critically analyze, evaluate and synthesize complex and multi-dimensional phenomena.

• Abilities

Upon completion of the lesson, the Students / students are able (i) to select the proper type of econometric model (specification), depending on the characteristics of the available data and the phenomena to be examined, (ii) apply the appropriate econometric methods and procedures.

Ability to search, process, analyze and synthesize data and information in order to develop – using econometric methods – policy proposals as regards spatial planning or target specific areas.

The course – through the use of quantitative / econometric methods – contributes to the achievement of scientific analysis capacity and development of criticism and self-criticism.

This is ensured by (i) the content of the lectures and (ii) the assignments / exercises given during the semester as well as (iii) the assessment’s system of the students.

The course consists of 13 lectures covering the most important fields of statistical and econometric analysis. More specifically, the course includes the following:

Lecture 1: Introduction to Econometrics: Definitions, Objectives, Methodological Approach, Model Specialization, Necessary Statistical Concepts (Correlation, Variance, Case Studies etc.).

Lecture 2: The Classic Linear Model: Basic assumptions, regression function (bivariate and trivariate model), Least Squares Method (LSM), regression coefficients, Model Evaluation (overall model evaluation and model evaluation)

Lecture 3: Curve estimation: From linear to non-linear curve. Implementation in SPSS – selection of the “effective” curve.

Lecture 4: The Generalized Linear Model (A): Mathematical modeling, multivariate sample evaluation, hypothesis tests, analysis and interpretation of results using a variety of examples. Application in SPSS.

Lecture 5: The Generalized Linear Model (B): Introduction of Structural Variables (pseudo-variables) and their control, Chow-Test. (i) The example of the Gravity model. Implementation in SPSS and EViews, (ii) Implementation of Nonlinear Model in SPSS (NonLinear Regression).

Lecture 6: Violations of the Most Important Linear Model Assumptions (A): Auto-Correlation, heteroskedasticity, Controls and Resolution.

Lecture 7: Violations of the Most Important Linear Model Assumptions (B): Multicollinearity, Modeling Errors, Controls and Resolution.

Lecture 8: Implementation of Models with SPSS and EViews

Lecture 9: Time Series: Estimates – Forecasts (Classical Methods of Analysis, Stochastic Analysis)

Lecture 10: Model with Panel data: Model with multidimensional data measured over time (Panel data)

Lecture 11: Models with Discrete and Restricted Dependent Variables: Probability Models, Probit – Logit Model.

Lecture 12: Examples of Discrete and Restricted Dependent Variables: Multinomial Logistic Regression (MLR)

Lecture 13: Implementation of Discrete and Restricted Dependent Variables with SPSS and EViews.

1. Test with multiple choice questions: 30%
2. Short Answer Questions: 10%
3. Written Homework: 40%
4. Oral presentation of the home work with power point: 20%

Short answer questions are applied every 15 days. The test with multiple choice questions is made during the 8th lecture. The student chooses a topic based on which he / she will create and apply an econometric model. In this context, meetings are regularly scheduled to allow the student to solve any questions they may have.

Short answer questions, individual work and oral presentation at the end of the semester allow to control that the students have understood the basic concepts / tools and methods.

The individual work certifies the ability of students to design, implement and interpret specific econometric models.

The course outline explicitly mentions how students are evaluated and is available on the open e-class of the University of Thessaly.

Students are systematically monitored during the semester and especially during the preparation of their individual work where meetings (“corrections”) are made on days and hours designated by the teachers after consultation with the students.

Transparency is fully guaranteed. If there is a divergence of opinion regarding their final assessment, the students have the right to request upgrading from the General Department of the Department.

• Ashenfelter O., Levine P.B., Zimmerman D.J. (2003), Statistics and Econometrics: Methods and Applications, New-York: Ed.
• Gujarati, D. and Porter, D. (2016), Οικονομετρία, αρχές και εφαρμογές (5η έκδοση), Αθήνα: εκδόσεις Τζιόλα.
• Wooldridge J. (2013), Εισαγωγή στην οικονομετρία, Αθήνα: Παπαζήση.
• Ανδρικόπουλος Α. (2003), Οικονομετρία: Θεωρία και εμπειρικές εφαρμογές, Τόμος Α, Αθήνα: Εκδόσεις Ευγ. Μπένου, 3η έκδοση.
• Κιντής Α. (1999), Στατιστικές και Οικονομετρικές Μέθοδοι, Αθήνα: Εκδ. Gutenberg.
• Μαυρομάτης Γ. (1999), Στατιστικά Μοντέλα και Μέθοδοι Ανάλυσης δεδομένων. Θεσσαλονίκη: University Studio Press, (Κεφ. 1 – 3).
• Also, in the context of the individual work, selected scientific articles are given that refer directly to the subject being examined by the student.