finance riskeconometricsforecastingmacro policy analysisstata r python
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical command
35% weight
Check depth in econometric methods: panel fixed effects, instrumental variables, difference-in-differences, VAR or DSGE work, plus fluency in Stata, R, EViews or Python statsmodels.
Evidence to listen for
Commands the instruments, models, or reporting standards the role turns on
Can build the analysis rather than only interpret someone else's
Knows the assumptions inside a model and which ones actually drive the answer
Fluent in the frameworks and disclosure regimes that apply
Five-point scoring guide
1
Poor
Cannot explain the instruments or standards they claim to work with.
2
Needs Improvement
Interprets others' analysis but cannot build or defend it.
3
Satisfactory
Solid working command; thin on unfamiliar structures or standards.
4
Very Good
Builds the analysis and knows which assumptions actually move the answer.
5
Excellent
Names the identification strategy and diagnostics used, discusses endogeneity and standard error choices without prompting, and cites specific model specifications.
02
Evaluation factor
Deals and deliverables that closed
25% weight
Probe concrete outputs: published forecasts, regulatory impact assessments, cost-benefit appraisals, expert reports, or quarterly outlooks, and what decision or publication each one fed.
Evidence to listen for
Names transactions, filings, or reports they worked, with size, counterparties, and their own scope
Distinguishes their contribution from the deal team's
Knows what happened afterwards, including what underperformed
Can describe one that fell over and why
Five-point scoring guide
1
Poor
No completed work; describes process rather than outcomes.
2
Needs Improvement
Involved in transactions but cannot state their own scope.
3
Satisfactory
Real deliverables with adequate ownership; outcomes described loosely.
4
Very Good
Named transactions or filings with clear personal scope and honest post-mortems.
5
Excellent
Points to named deliverables with dates and audiences, including forecast accuracy versus outturn and how findings changed a policy or pricing decision.
03
Evaluation factor
Risk judgement
25% weight
Assess how they handle data limitations and uncertainty: revisions to national accounts, structural breaks, small samples, and how they communicate confidence intervals or scenario ranges.
Evidence to listen for
Distinguishes a modelled risk from a real one
States confidence and what would change their view
Comfortable disagreeing with a number that suits everybody
Knows the limits of the data behind a projection, especially over long horizons
Five-point scoring guide
1
Poor
Treats model output as truth; no sense of data limits.
2
Needs Improvement
Reports numbers without qualifying them; avoids unwelcome conclusions.
3
Satisfactory
Reasonable judgement; qualifies findings when prompted.
4
Very Good
States confidence unprompted and will hold an unpopular position on evidence.
5
Excellent
Distinguishes correlation from causal claims, flags where their estimates would break, and presents scenarios rather than a single false-precision number.
04
Evaluation factor
Explaining it to decision-makers
15% weight
Test the translation to non-economists: briefing ministers, boards, or clients; chart choices; handling challenge from people who dislike the conclusion.
Evidence to listen for
Explains a technical position to an investment committee, board, or regulator so they can act on it
Writes to the standard the audience is held to
Handles challenge without either caving or digging in
Works across legal, operations, and external counterparties
Five-point scoring guide
1
Poor
Cannot communicate beyond technical peers.
2
Needs Improvement
Explanations lose the audience or oversimplify to the point of error.
3
Satisfactory
Adequate with familiar audiences; less effective under challenge.
4
Very Good
Explains clearly to committees and regulators and holds up under challenge.
5
Excellent
Explains an elasticity, multiplier, or output gap in plain terms, and describes defending a controversial estimate under scrutiny without overstating it.
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