Econometrics and Introduction to R
Full Time// 1st Semester, Course Code: ΜΕΧΡΗ329
Part Time // 2nd Semester, Course Code: ΜΕΧΡΗ-Μ329
Credits: 6
Learning Outcomes
Upon successful completion of the course, the student
(a) will know the fundamental assumptions for the application of the linear model and its estimation by the method of least squares, but also by alternative methods,
(b) will be able to interpret the estimated parameters of the linear model as well when the logarithm of an explanatory or dependent variable is used. It will also be able to draw conclusions from the standard tables produced by econometric software packages,
(c) will be aware of the effects of violating the classical assumptions, such as when there is heteroscedasticity, multicollinearity or when an explanatory variable has been omitted,
(d) will be able to test for heteroscedasticity and know how to deal with it;
(e) will be able to use dummy variables and interpret the estimated coefficients,
(f) will know the purpose and basic properties of important non-linear models (probit, logit),
(g) will know the basic principles of the Instrumental Variables method for treating the problem of endogeneity,
(h) will have acquired a basic familiarity with the R language and will know its basic functions, which are needed data processing, and be able to use R in applications of the taught econometric methods
General Competences
Within the framework of the combined skills that the graduate will acquire by attending all the courses of the study program, this course aims at the graduate to acquire abilities:
(a) in the search for, analysis and synthesis of data and information, with the use of the necessary technology,
(b) in decision-making
(c) in working independently
(d) to promote free, creative and inductive thinking
(e) in exercising criticism and self-criticism
Course Content
The course focuses on the following sections:
1) Introduction to Linear Regression
The nature of Econometrics
The statistical generating mechanism and regression models.
Linear regression models, simple regression models, the classic multiple regression model.
The Simple Linear Regression Model
2) The classic assumptions.
Least squares estimators, sample moments estimators, method of moments.
Properties of estimators, Gauss-Markov theorem, distributions of estimators.
Multiple Regression Analysis
3) The case of k independent variables
Interpreting the least squares regression equation
Expected value and variance of least squares estimators
Hypothesis testing and confidence intervals
Checking multiple linear constraints
Asymptotic properties of least squares: Consistency and efficiency of estimators
Consequences of multicollinearity
Heteroscedasticity
4) Implications for the least squares estimator
Heteroscedasticity “robust” inference
Testing for Heteroscedasticity
Weighted and Generalized Least Squares
5) Regression Analysis with Qualitative Information: binary (or dummy) variables
Regression with one dummy (binary) variable
Dummy variables for multiple categories
A binomial dependent variable: the linear probability model
6) Special issues
The Probit and Logit models
Endogeneity and the method of Instrumental Variables
During the course there will be an introduction to the R language and the use of the RStudio interface, including:
Basic R functions for data manipulation
Examples are presented using the appropriate R functions for regression analysis, according to the course outline.
Special emphasis is placed on the interpretation of the results obtained from the use of these functions.
Student Performance Evaluation
Formative and conclusive evaluation is carried out. The final evaluation of the students is done by a written exam or an oral exam. It is based on problem solving, short-answer questions and open-ended questions.
Bibliography
Suggested Bibliography
- Tutor’s Notes
- Introductory Econometrics: A Modern Approach, 6th edition (J. Woodrifge) – Greek edition, Papazisis ed.)
Related Academic Journals
- Econometric Reviews, Econometric Theory
- Journal of Econometrics
- Journal of Applied Econometrics
Master of Science (M.Sc.) in
«Banking and Finance»
Specialization in
«Banking and Financial Management»