Learning Objectives
5 objectives- Understand the fundamental concepts and purpose of econometrics and its role in economic analysis.
- Develop skills in collecting, organizing, and analyzing different types of economic data.
- Acquire proficiency in linear regression analysis, including model estimation, assumptions, and hypothesis testing.
- Recognize and address common econometric issues such as multicollinearity, heteroscedasticity, and endogeneity.
- Apply advanced econometric techniques including time series, panel data analysis, and causal inference for policy evaluation.
Content Outline
PreviewUnit 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
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