Econometrics I (2235.YR.015774.1)
General information
Type: |
OBL |
Curs: |
1 |
Period: |
S semester |
ECTS Credits: |
3 ECTS |
Teaching Staff:
Prerequisites
Basics of Calculus and of Matrix Algebra. Quantitative univariate and bivariate Methods, Exploratory Data Analysis, Basics of Probability, Sampling distributions, and Statistical inference: Estimation and Testing.
COURSE CONTRIBUTION TO PROGRAM
Upon completion of this course the participants will be able to:
- Use statistical software to aid in managerial decision making based on knowledge of the variability and the interrelationships (dependence and interdependence) of the different aspects that could be of interest in a particular study.
- Be familiar with the literature on univariate and multivariate analysis as well as most common potential drawbacks of basic estimation techniques.
- Use the tools developed in the course to start their own empirical research.
- Read academic empirical research papers.
Course Learning Objectives
Upon completion of this course the participants will be able to:
- Use some statistical software (R, Stata, SPSS) to aid in managerial decision making based on knowledge of the variability and the interrelationships (dependence and interdependence) of the different aspects that could be of interest in a particular study.
- Be familiar with the literature on univariate and multivariate analysis as well as most common potential drawbacks of basic estimation techniques.
- Use the tools developed in the course to start their own empirical research.
- Read academic empirical research papers.
CONTENT
1. Linear Regression with One Regressor |
2. Hypothesis testing and confidence intervals |
3. Linear Regression with Multiple Regressors |
4. Regression with binary dependent variable |
5. Binary models, Interactions and Non-linear regression functions |
6. OLS shortcomings I: Heteroskedasticity, Autocorrelation, and Clustering |
7. OLS shortcomings II: Assessing Studies Based on Multiple Regression |
Relation between Activities and Contents
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
| Two problem sets (40%) |
|
|
|
|
|
|
|
| Final Exam (60%) |
|
|
|
|
|
|
|
Methodology
The course begins by covering the basics of one single regressor and multiple regressor analysis, including Hypothesis testing and Significance tests.
The second part of the course is devoted to explore the shortcomings of linear regression analysis.
We will use a combination of lecture slides, empirical software applications and journal articles when needed.
The focus will be in the interpretation of the estimators and results and the use of the various techniques in light of the problems that may arise.
ASSESSMENT
ASSESSMENT BREAKDOWN
| Description |
% |
| Two problem sets (40%) |
40 |
| Final Exam (60%) |
60 |
Assessment criteria
Two problem sets (one after the first part of the course and a second one after the second part of the course) (20% each, 40% total).
Final Exam (60%).
Bibliography
Stock and Watson (2012), Introduction to Econometrics, Pearson, 3rd Edition.
Verbeek M. (2004), A Guide to Modern Econometrics, Wiley, 2nd Edition.
Jeffrey Wooldridge (2nd edition), Econometric Analysis of Cross Section and Panel Data.
Advanced:
Angrist and Pischke, Mostly Harmless Econometrics: https://www.mostlyharmlesseconometrics.com/
Casual Inference: https://www.casualinf.com/
