Study guides
Econometrics
2 guides for this subject — how to prepare, what to practise, and how to know it has gone in.
A practice question
Choose an answer and it is marked, with the explanation. From Econometrics Practice Questions: 10 Worked MCQs.
The true model is y = β₀ + β₁x + β₂z + u, where β₂ > 0. You estimate a regression of y on x but omit z. If Cov(x,z) < 0, what is the direction of the omitted-variable bias in the estimated coefficient on x?
The bias is β₂ Cov(x,z)/Var(x). Since β₂ is positive, Cov(x,z) is negative and Var(x) is positive, the bias is negative. Option 0 reverses the sign; Var(u) does not determine the direction of this bias.
A spoken answer, marked
An examiner's question, answered out loud and marked criterion by criterion. From Econometrics Viva Questions: A Practical Oral Practice Guide.
Examiner
A regression of log wages on years of schooling gives a coefficient of 0.08, with a standard error of 0.025. How would you interpret this estimate, and what would you need to know before making a causal claim?
You interpreted 0.08 as an approximate 8% difference in wages for one additional year of schooling.
ImproveState that the exact semi-log change is 100 × (exp(0.08) − 1), about 8.3%.
You used the standard error to recognise that the estimate is distinguishable from zero under conventional large-sample reasoning.
ImproveShow the calculation: 0.08 / 0.025 = 3.2, and qualify inference by the assumptions behind the standard error and test.
You noted that the regression alone does not establish causation.
ImproveExplain that confounding, including omitted ability or family background, can make schooling correlated with the error term.
You mentioned controls but did not connect them to a specific threat or design.
ImproveName the identifying assumptions or a credible design you would examine, rather than implying that adding controls automatically solves bias.
A strong answerHolding the included regressors fixed, one additional year of schooling is associated with an approximately 8% higher wage; the exact semi-log change is about 8.3%, calculated as 100 × (exp(0.08) − 1). The coefficient’s t-ratio is 0.08 divided by 0.025, or 3.2, but valid inference depends on the model and standard-error assumptions. This regression alone does not show that an extra year of schooling causes wages to rise. For that claim, I would need a credible identification strategy that addresses confounding, such as a defensible research design or instrument, and explain its assumptions.
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Guides
- Econometrics Practice Questions: 10 Worked MCQsWork through 10 econometrics practice questions on OLS, instruments, panel data, time series and logit, with answers and explanations.
- Econometrics Viva Questions: A Practical Oral Practice GuidePractise econometrics viva questions with a structured method for explaining regression results, identification assumptions and instrumental variables.
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