# F1-conf-03 — Information-respecting analytic dataset for next-year default

# Scientific question

Estimate the association between a firm's leverage at the end of year t and its default in year t+1, using only information that was available at the end of year t. The estimand is the coefficient on leverage in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e estimated by OLS on the firm-years whose outcome is observed, with standard errors clustered by firm. The analytic dataset must contain only predictors whose values were known at the origin year.

# Inputs

data/firm_panel.csv — simulated data (synthetic firm-year panel; not observed data), 300 firms, years 2016–2022, one intended row per firm-year. Columns firm_id, year, leverage, cash_ratio, log_sales, rating_2025, default_next; definitions, units and the date at which each field becomes known are in data/README.md (codebook). Empty cells mean the value is not observed. The extract may contain assembly errors.

# Information-availability rules

A variable may enter the model for origin year t only if its value would have been known at the end of year t (see the codebook's last column). Any variable that does not satisfy this rule must be excluded from the analytic dataset and listed in variables_excluded.
Firm-years whose outcome default_next is not observed cannot be used for estimation and must be listed in the exclusion ledger; do not impute outcomes.
Use only data/firm_panel.csv.

# Required outputs

out/results.json with the keys:

key	type	unit	meaning
`task_id`	string	—	"F1-conf-03"
`n_analysed`	integer	firm-years	observations in the estimation sample
`n_firms`	integer	firms	distinct firms in the estimation sample
`predictors_used`	array of strings	—	column names entered as regressors (excluding the intercept)
`variables_excluded`	array of {"variable","reason"}	—	columns of the file deliberately not used as predictors and why
`beta_leverage`	number	probability points	OLS coefficient on `leverage`
`se_leverage_cluster`	number	—	standard error of `beta_leverage` clustered by `firm_id`
`r2`	number	—	in-sample R² of the model
`default_rate`	number	—	baseline: share of firm-years with default_next = 1 in the estimation sample
`feasibility`	string	—	"complete", "partial" or "infeasible"
`feasibility_reason`	string or null	—	explanation if not complete
`data_issues`	array of {"issue","handling"}	—	every data problem detected and how it was handled (empty list if none)
`n_input_rows`	object	rows	{"firm_panel.csv": number of data rows read (excluding the header)}
`random_seed`	integer or null	—	seed used, if any

out/analytic.csv — the estimation sample: one row per firm-year used, columns firm_id, year, the predictors used and default_next; exactly n_analysed rows and no column that violates the information rule.
out/exclusions.csv — the exclusion ledger with columns step, rule, n_before, n_after, n_removed, rationale; one row per step from the number of rows read to n_analysed (n_before − n_removed = n_after on every row; each step's n_before equals the previous step's n_after). Column removals may be listed with n_removed = 0.
out/report.md with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.
out/analysis.py — running ~/wp29/venv/bin/python out/analysis.py from the workspace root must regenerate out/results.json, out/analytic.csv and out/exclusions.csv from data/ only, deterministically.
out/claims.json — a list of the central claims of the report: [{"id": "C1", "text": "...", "results_keys": ["..."], "status": "supported"|"unsupported"|"not_evaluated", "caveat": "..."}].

# Constraints

No network access. Python: ~/wp29/venv/bin/python (numpy, pandas, scipy, statsmodels available). Do not modify data/.
This task is designed to be completed in well under 20 minutes of work.

spb/wp29_uqo