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Econometrics

Turn economic data into insights through statistical and mathematical analysis.

Economics Courses

65 lessons / quizzes

Topics

Exam‑style questions with instant feedback.

Section 1: Foundations of Probability and Statistics

1: Probability Spaces and Random Variables

2: Key Distributions: Normal, t, F, and Chi-Square

3: Sampling Distributions and the Central Limit Theorem

4: Point Estimation: Unbiasedness, Consistency, and Efficiency

5: Hypothesis Testing: Logic and Mechanics

6: Confidence Intervals and Their Interpretation

7: Joint, Conditional, and Marginal Distributions; Covariance and Correlation

Section 2: Simple Linear Regression

1: The Simple Linear Regression Model and Ordinary Least Squares

2: Gauss-Markov Assumptions and the BLUE Property

3: Interpreting Coefficients and Goodness-of-Fit

4: Inference in Simple Linear Regression: t-tests and Confidence Intervals

5: Prediction and Forecasting with Simple Linear Regression

6: Potential Pitfalls in Simple Regression: Nonlinearity, Outliers, and Influential Observations

Section 3: Multiple Linear Regression

1: Model Specification and Matrix Form of OLS

2: Interpreting Partial Coefficients and the Omitted Variable Bias

3: Inference in Multiple Regression: F-tests and Linear Restrictions

4: Model Selection: Adjusted R², AIC, BIC, and Stepwise Methods

5: Dummy Variables and Interaction Effects

6: Multicollinearity: Detection, Consequences, and Remedies

Section 4: Violations of Classical Assumptions

1: Heteroskedasticity: Detection and Robust Standard Errors

2: Weighted Least Squares and Feasible GLS

3: Autocorrelation: Detection and Newey-West Standard Errors

4: Cochrane-Orcutt and Prais-Winsten Estimation

5: Endogeneity and Omitted Variable Bias Revisited

6: Functional Form Misspecification and the RESET Test

7: Outliers, Leverage, and Influential Points in Multiple Regression

Section 5: Causal Inference and Identification

1: The Potential Outcomes Framework and Average Treatment Effects

2: Randomized Experiments and the Difference in Means Estimator

3: Instrumental Variables: Intuition, Two-Stage Least Squares, and Validity

4: Difference-in-Differences (DiD) Design

5: Regression Discontinuity Design (RDD)

6: Matching and Propensity Score Methods

7: Synthetic Control and Modern Causal Methods (Brief Overview)

Section 6: Time Series Econometrics

1: Time Series Data: Properties and Autocorrelation Function

2: AR and MA Models: Stationarity and Invertibility

3: ARIMA Models: Identification, Estimation, and Forecasting

4: Unit Roots and Dickey-Fuller Tests

5: Cointegration and Error Correction Models

6: Volatility Modeling: ARCH and GARCH

Section 7: Special Topics and Applications

1: Panel Data Models: Fixed Effects and Random Effects

2: Limited Dependent Variables: Logit, Probit, and Marginal Effects

3: Quantile Regression and Nonparametric Methods (Overview)

4: Practical Econometrics: Data Cleaning, Reproducibility, and Communication

What you’ll achieve

Learn regression analysis and statistical modelling techniques.

Explore time series, panel data, and forecasting methods.

Develop practical skills in analysing real economic data.

Understand how to test economic theories using evidence.Understand how to test economic theories using evidence.

Build expertise valuable for careers in research, finance, and policymaking.

Course illustration

Course overview

Everything you need to excel in Econometrics

Econometrics is the application of statistical and mathematical methods to economic data, helping test theories, estimate relationships, and forecast trends. This course introduces regression models, hypothesis testing, panel data analysis, and time series methods. Students will learn to handle real-world data, interpret results, and critically evaluate empirical studies. By bridging theory with practice, econometrics provides the tools to make informed decisions in policy, business, and research.

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