Applied Econometrics Tutor in Singapore – Syllabus

1. Introduction to Econometrics

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  • Nature, scope, and objectives of econometrics
  • The role of data in empirical economics
  • Types of data: cross-sectional, time series, panel
  • Structure of econometric models (deterministic vs. stochastic)
  • Classical assumptions and their importance
Applied Econometrics Tutor in Singapore

2. Simple Linear Regression

  • Specification of the two-variable regression model
  • Ordinary Least Squares (OLS): estimation and interpretation
  • Properties of OLS estimators (BLUE)
  • Hypothesis testing: t-test, F-test, confidence intervals
  • Goodness-of-fit: R² and adjusted R²

3. Multiple Linear Regression

  • Extension to multiple regressors
  • Interpretation of coefficients
  • Multicollinearity: detection and remedies
  • Testing joint significance of regressors
  • Functional form and model specification

4. Violations of Classical Assumptions

  • Heteroscedasticity: nature, detection (Breusch–Pagan, White test), consequences, remedies (robust standard errors, GLS)
  • Autocorrelation: sources, detection (Durbin–Watson, Breusch–Godfrey), remedies (Cochrane–Orcutt, Newey–West)
  • Multicollinearity revisited with variance inflation factors (VIF)
  • Specification errors: omitted variables, irrelevant variables, measurement error

5. Dummy Variables and Qualitative Response Models

Applied Econometrics Tutor in Singapore

  • Use of dummy variables in regression
  • Interaction effects and structural change
  • Linear probability model (LPM), logit, and probit models

6. Time Series Econometrics

  • Stationarity and non-stationarity
  • Unit root tests (ADF, PP)
  • AR, MA, ARMA, and ARIMA models
  • Forecasting techniques
  • Cointegration and error correction models (ECM)

7. Panel Data Econometrics

  • Pooled regression model
  • Fixed effects vs. random effects models
  • Hausman test
  • Applications in microeconomics and policy analysis
  • Applied Econometrics Tutor in Singapore

8. Simultaneous Equations Models

  • Identification problem
  • Two-Stage Least Squares (2SLS)
  • Limited Information vs. Full Information methods

9. Applications of Econometrics

  • Policy evaluation and impact assessment
  • Demand and supply estimation
  • Wage equations, education and earnings models
  • Growth regressions and development economics applications

10. Software and Practical Component

  • Hands-on with statistical/econometric software: Stata, R, EViews, or Python
  • Data cleaning and descriptive analysis
  • Running regressions and interpreting output
  • Project/term paper using real-world datasets

Assessment Methods:

  • Midterm and final exams (theory + applications)
  • Problem sets using software
  • Empirical project/report writing
  • we provide Applied Econometrics in singapore all locality.

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