ECONOMICS-07 · Undergraduate · ENGLISH · PLANNED
State the question and assumptions, draw the model, analyse evidence and test competing explanations.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Economics | Econometrics and Causal Inference
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Sign in to save this learning pathPractical outcome: Estimate and interpret a relationship in a supplied synthetic dataset.
Explain simple regression using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Explain ordinary least squares using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Explain coefficient interpretation using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Explain residuals using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Use the first concepts from this section. Estimate and interpret a relationship in a supplied synthetic dataset. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain multiple regression using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Explain dummy variables using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Explain functional forms using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Explain prediction intervals using a visual and one worked real-world example. Context: Estimate and interpret a relationship in a supplied synthetic dataset. End with a prediction or check-for-understanding.
Estimate and interpret a relationship in a supplied synthetic dataset. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Simple regression, Ordinary least squares, Coefficient interpretation and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Diagnose flawed conclusions from a fictional wage regression.
Explain omitted-variable bias using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Explain heteroskedasticity using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Explain multicollinearity using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Explain measurement error using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Use the first concepts from this section. Diagnose flawed conclusions from a fictional wage regression. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain selection bias using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Explain robust standard errors using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Explain model specification using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Explain external validity using a visual and one worked real-world example. Context: Diagnose flawed conclusions from a fictional wage regression. End with a prediction or check-for-understanding.
Diagnose flawed conclusions from a fictional wage regression. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Omitted-variable bias, Heteroskedasticity, Multicollinearity and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare identification strategies for a hypothetical training programme.
Explain randomised experiments using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Explain instrumental variables using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Explain difference-in-differences using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Explain regression discontinuity using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare identification strategies for a hypothetical training programme. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain fixed effects using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Explain panel data using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Explain pre-analysis plans using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Explain sensitivity checks using a visual and one worked real-world example. Context: Compare identification strategies for a hypothetical training programme. End with a prediction or check-for-understanding.
Compare identification strategies for a hypothetical training programme. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Randomised experiments, Instrumental variables, Difference-in-differences and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Compare identification strategies for a hypothetical training programme. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Econometrics and Causal Inference, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Economic diagrams, synthetic datasets, calculations and evidence-based policy memos. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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