Econometrics

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
5. Two-Variable Regression Analysis: Some Basic Ideas : Basic Data Handling; 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
14. Statistical Aspects of Regression: Which factors affect the accuracy of the estimate?
15. Calculating the confidence interval
16. Testing the significance of regression coefficients: the t test
17. Hypothesis testing involving R2: the F test
18. Multiple regression: estimation and hypothesis tests
19. Regression as a best fitting line
20. OLS estimation of the multiple regression model
21. Statistical aspects of multiple regression
22. Interpreting OLS estimates
23. Pitfalls of using simple regression in a multiple regression context
24. Omitted variables bias
25. Multicollinearity
26. Extensions of the Two-Variable Linear Regression Model: Regression Trough the Origin
27. Scaling and Units of Measurement
28. Functional Forms of Regression Models such as Double-log, Semilog, and Reciprocal Models
29. Regression with Dummy Variables : Simple regression with a dummy variable
30. Multiple regression with dummy variables
31. Multiple regression with dummy and non-dummy explanatory variables
32. Interacting dummy and non-dummy variables
33. What if the dependent variable is a dummy?
34. Applications.