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.

Published at : Updated

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