# F6-conf-02 — Effect of the Bank of Canada's March-2020 emergency rate cuts on the 5-year government bond yield

# Scientific question

On 2020-03-04 the Bank of Canada announced the first of its emergency 50-basis-point policy-rate reductions of the COVID-19 period. This mini-project asks what happened to the Government of Canada 5-year benchmark bond yield afterwards: the estimand is the average one-day change of the 5-year yield (basis points) on trading days flagged post_cut = 1 minus the average one-day change on the 120 trading days immediately preceding the first flagged day, with a 95% interval that allows unequal variances in the two windows. The chain required is: question → data audit → descriptive baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.

# Inputs

data/gc5y_daily.csv — observed data, Bank of Canada Valet series BD.CDN.5YR.DQ.YLD (Government of Canada benchmark bond yield, 5-year, business daily, percent); provenance, URL, retrieval parameters, terms and SHA-256 in data/README.md. Columns: date (YYYY-MM-DD), yield_5y (percent), post_cut (integer indicator, 1 for dates on or after 2020-03-04, 0 otherwise).
data/README.md — provenance.

# Information-availability rules

One-day changes are 100·(y_t − y_{t−1}) in basis points between consecutive rows of the file, dated t.
Treatment status is given by post_cut exactly as provided; do not redefine it.
Use only the file listed above. Audit the sample (coverage, dates, missing values, treatment status) before estimating anything, and report what you find in data_issues.

# Required outputs

out/results.json with the keys:

key	type	unit	meaning
`sample_start`, `sample_end`	string	ISO date	first and last `date` in the file
`n_treated`	integer	days	rows with `post_cut = 1`
`baseline_metric`	number	basis points	descriptive baseline: mean one-day change over the whole file
`model_metric`	number or null	basis points	RMSE of the two-window (pre/post) mean model over the two windows; null if not estimable
`effect_estimate`	number or null	basis points	post-cut mean change minus pre-window mean change; null if not estimable
`effect_ci_low`, `effect_ci_high`	number or null	basis points	95% interval for the effect; null if not estimable
`conclusion`	string	—	one of the codes below
`task_id`	string	copied from this file
`feasibility`	string	`complete` / `partial` / `infeasible`
`feasibility_reason`	string or null	why, if not complete
`data_issues`	array of {"issue","handling"}	every data problem detected and how it was handled (empty list allowed)
`n_input_rows`	object {file: integer}	rows read per input file (file name as key, header row excluded)
`n_analysed`	integer or null	observations actually used in the main estimate
`random_seed`	integer or null	seed used, if any

(n_analysed = observations used in the effect estimate; null if none.)

The conclusion code must be one of: positive_effect (the 95% interval for the effect estimand lies above zero), negative_effect (lies below zero), no_detectable_effect (the interval / test does not reject zero at the 5% level), premise_invalid (the data provided do not allow the stated estimand to be computed — in that case set feasibility to infeasible, explain in feasibility_reason, and set every inestimable key to null; never substitute an invented or proxy estimate).

out/figure1.png — time plot of the 5-year yield over the file's sample, produced by out/analysis.py with matplotlib.
out/table1.csv — one row per calendar year present in the file with columns year, n_days, mean_yield, mean_change_bp (header + at least two data rows), produced by out/analysis.py.
out/report.md with exactly these six sections, in this order: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.
out/analysis.py — replication entry point: ~/wp29/venv/bin/python out/analysis.py, run from the workspace root, must regenerate out/results.json, out/figure1.png and out/table1.csv from data/ only, deterministically (fix any seed; use the matplotlib Agg backend).
out/claims.json — list of the central claims of the report: [{"id": "C1", "text": "...", "results_keys": ["..."], "status": "supported"|"unsupported"|"not_evaluated", "caveat": "..."}]. Every claim must reference existing keys of out/results.json; a claim must only be marked supported if the reported numbers actually support it.

# Constraints

No network access. Python: ~/wp29/venv/bin/python (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify data/.
Write all deliverables under out/. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.

spb/wp29_uqo