Applied Econometrics
Applied Econometrics is the application of econometric techniques and models to analyze real-world economic data and solve practical problems in economics and related fields.
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Model Building: Developing econometric models to analyze economic data.
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Data Analysis: Applying statistical methods to interpret economic data.
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Estimation Techniques: Using techniques like OLS, GLS, and IV for estimating economic relationships.
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Hypothesis Testing: Testing economic theories and hypotheses with statistical methods.
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Forecasting: Predicting future economic conditions based on historical data.
Before learning Applied Econometrics, you should have:
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Basic Economics: Understanding of economic concepts and theories.
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Statistics: Knowledge of basic statistical methods and concepts.
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Mathematics: Familiarity with algebra and calculus, especially for model formulation.
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Data Analysis: Skills in analyzing and interpreting data.
- The Nature of Applied Econometrics
- Data Considerations and Ordinary Least Squares Estimation of Single-Equation Econometric Models
- Interpretation and Use of Estimated Coefficients and Forecasting with Single-Equation Econometric Models
- Common Tests of Hypotheses in Applied Econometrics
- Use of Dummy (Indicator) Variables in Applied Econometrics
- Diagnostic Checks - Autocorrelation or Serial Correlation
- Diagnostic Checks - Heteroscedasticity