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The main objectives of this course to revise and strengthen students’ knowledge of univariate and multivariate statistics; to obtain a full understanding of the classical linear regression model; to provide students with a working knowledge of regression analysis under non-classical assumptions on the disturbance; to provide the necessary mathematical and statistical tools needed to conduct theoretical econometric analysis at an advanced level.
What is Econometrics? Types of Economic Data. Causality vs. Correlation. Review of Statistical Concepts. Introduction to Stata. The Simple/Multiple Linear Regression Model. Model and Assumptions. Ordinary Least Squares (OLS) Estimator. Goodness-of-fit and R-squared. Properties of OLS. Omitted Variables Bias. Multicollinearity. Gauss-Markov Theorem: Efficiency of OLS. Statistical Inference for OLS. Confidence Intervals. Functional Forms And Selection Of Regressors. Dummy Explanatory Variables. Heteroskedasticity and Weighted Least Squares.
Upon succesful completion of this course, a student will be able to
1. Demonstrate understanding of verbal, graphical, mathematical and econometric representation of economic ideas and analysis, including the relationship between them.
2. Demonstrate more extensive knowledge and skills of quantitative economics and econometrics.
3. Apply complex ideas to solve problems; reason logically and work analytically; perform with high levels of accuracy.
4. Apply mathematical, statistical and graphical techniques in an appropriate manner and analyse and solve complex problems accurately.
5. Relate economic questions to empirical observations and try to deal with those using econometric models based on sound hypotheses.
6. Have a working knowledge of a statistical software and use it to address empirical questions.
7. Demonstrate the ability to apply econometric principles by writing a quality paper and giving oral presentations of the findings of the paper as an individual and as a group member.