People Innovation Excellence

# ECONOMETRICS (2/1 Credits)

## Learning Outcomes:

On successful completion of this course, student will be able to: Understand econometrics theory and its application; Use Statistics software to analyse econometrics model; Analyse econometric model using real data.

## Topics:

1. Introduction: An Overview of Econometrics: The nature of econometrics;
2. Types of Data;
3. Short history of econometrics;
4. The Nature of Regression Analysis. Two-Variable Regression Analysis: Some Basic Ideas: Basic Data Handling;
5. Basic Data Analysis;
6. Understanding correlation;
7. Correlation and causality;
8. Correlation between several variables;
9. An Introduction to Simple Regression: Regression as a best fitting line;
10. Derivation of least squares estimators;
11. Interpreting OLS estimates;
12. Measures the fit of a Regression Model;
13. Nonlinearity in regression. Statistical Aspects of Regression: Which factors affect the accuracy of the estimate?;
14. Calculating the confidence interval;
15. Testing the significance of regression coefficients: the t test;
16. Hypothesis testing involving R2: the F test;
17. Multiple regression: estimation and hypothesis tests;
18. Regression as a best fitting line;
19. OLS estimation of the multiple regression model;
20. Statistical aspects of multiple regression;
21. Interpreting OLS estimates;
22. Pitfalls of using simple regression in a multiple regression context;
23. Omitted variables bias;
24. Multicollinearity. Extensions of the Two-Variable Linear Regression Model: Regression Trough the Origin;
25. Scaling and Units of Measurement;
26. Functional Forms of Regression Models such as Double-log, Semilog, and Reciprocal Models. Regression with Dummy Variables: Simple regression with a dummy variable;
27. Multiple regression with dummy variables;
28. Multiple regression with dummy and non-dummy explanatory variables;
29. Interacting dummy and non-dummy variables;
30. What if the dependent variable is a dummy?;
31. Applications.

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