Unit 2505: Introduction to Econometrics

1. Introduction to Econometrics

• Definition and scope of econometrics
• Purpose and applications in economics
• Relationship between econometrics and economic theory
• Importance of statistical methods in economic data analysis

2. Data Collection and Sources in Econometrics

• Types of econometric data:
  • Time series data
  • Cross-sectional data
  • Panel data
• Data collection methods and challenges
• Organizing and preparing data for analysis

3. Linear Regression Analysis

• Concept and formulation of linear regression models
• Estimation methods (Ordinary Least Squares - OLS)
• Interpretation of regression coefficients
• Assessing model fit (R-squared, adjusted R-squared)
• Making predictions using regression models

4. Assumptions of Linear Regression

• Key assumptions:
  • Linearity
  • Independence of errors
  • Homoscedasticity (constant variance of errors)
  • Normality of error terms
• Implications of assumption violations
• Diagnostic tests and remedies

5. Hypothesis Testing in Econometrics

• Formulating hypotheses for regression coefficients
• t-tests for individual coefficients
• F-tests for overall model significance
• Testing model specification
• Interpreting test results and p-values

6. Multicollinearity and Heteroscedasticity

• Definition and causes of multicollinearity
• Detecting multicollinearity (Variance Inflation Factor - VIF)
• Addressing multicollinearity issues
• Understanding heteroscedasticity
• Detection methods (Breusch-Pagan, White tests)
• Corrective measures (robust standard errors, transformations)

7. Time Series Analysis

• Characteristics of time series data
• Autocorrelation and its detection
• Stationarity and unit root tests
• Trend analysis and decomposition
• Econometric models for time series forecasting (AR, MA, ARIMA)

8. Panel Data Analysis

• Introduction to panel data structure
• Advantages of panel data over pure cross-sectional or time series
• Fixed effects and random effects models
• Estimation techniques
• Interpretation of panel data model results

9. Instrumental Variables and Endogeneity

• Concept of endogeneity and its sources
• Consequences of endogeneity in regression analysis
• Instrumental variable (IV) estimation
• Criteria for valid instruments
• Two-stage least squares (2SLS) method

10. Causal Inference and Econometric Policy Evaluation

• Principles of causal inference in econometrics
• Methods for causal effect estimation (difference-in-differences, regression discontinuity)
• Challenges in policy evaluation
• Application of econometric methods to assess policy impacts
• Interpretation of causal analysis results