# F3-conf-01 — Forecasting Canadian monthly GDP growth under publication lags

# Scientific question

At the end of each month, how well can the year-over-year growth of Canadian monthly real GDP one and three months ahead be forecast from the information that is actually published at that time, and does a model that also uses the (more timely) Labour Force Survey employment growth beat the naive forecast? The estimands are the out-of-sample RMSE and MAE, over a fixed set of 48 rolling (expanding-window) origins and horizons h ∈ {1, 3}, of (a) the naive forecast and (b) one model of your choice, and the relative improvement 1 − RMSE_model / RMSE_naive per horizon.

# Inputs

data/canada_gdp_employment.csv — observed data (Statistics Canada, current vintage, retrieved 2026-10-30 UTC via the Web Data Service; see data/README.md for vectors, URL, SHA-256 and licence). Columns: month (YYYY-MM-01), gdp_level (monthly real GDP at basic prices, chained 2017 dollars, SA, millions), gdp_yoy (target: year-over-year growth of gdp_level, percent, 4 decimals), emp_level (LFS employment, SA, thousands), emp_yoy (predictor: year-over-year employment growth, percent, 4 decimals). 345 monthly rows, 1998-01 to 2026-09; GDP columns are empty after the last published GDP month (2026-07), employment runs to 2026-09.

# Information-availability rules

Forecast origins: the 48 months t = 2022-05-01, 2022-06-01, …, 2026-04-01 (identified by their month value). The origin is the end of month t. Horizons h ∈ {1, 3}; the target of origin t at horizon h is gdp_yoy of month t + h.
Publication lags (documented in data/README.md): monthly GDP for a reference month is published about two months after the end of that month; LFS employment for a reference month is published in the first/second week of the following month. Therefore, at origin t, the GDP columns are available only for months ≤ t − 2 and the employment columns only for months ≤ t − 1. A forecast made at origin t (its estimation sample, its model selection, its transformations and the values plugged into the forecast) may use only these observations. Training targets must also be observed at the origin.
Naive baseline: for both horizons, the forecast is the most recent GDP growth value available at the origin, i.e. gdp_yoy of month t − 2.
The errors are computed on the data as given (current vintage); the exercise simulates publication timing, not historical first-release values — do not claim real-time validity.

# Required outputs

out/forecasts.csv — the forecast ledger, one row per (origin, horizon, model), columns: origin_date (YYYY-MM-DD, the month of the origin), target_date, horizon (integer), forecast, actual (the gdp_yoy value of the target month), model (naive or the value of model_name), last_target_date_used (YYYY-MM-DD: the latest gdp_yoy month entering the forecast, estimation included), last_predictor_date_used (YYYY-MM-DD: the latest emp_yoy month used, or empty if the model does not use employment). Expected: 48 × 2 × 2 = 192 rows.
out/results.json with the keys:

key	type	unit	meaning
`task_id`	string	—	"F3-conf-01"
`model_name`	string	—	short name of your model, used in the `model` column
`model_description`	string	—	one-sentence specification (lags, predictors, estimation sample, method)
`n_origins`	integer	—	48
`first_origin`, `last_origin`	string	date	first and last origin dates
`last_target_date_available`	string	date	latest `gdp_yoy` month available at the last origin (2026-04-01) under the rules
`last_predictor_date_available`	string	date	latest `emp_yoy` month available at the last origin under the rules
`rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3`	number	percentage points	naive errors per horizon
`rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3`	number	percentage points	your model's errors per horizon
`rel_improvement_rmse_h1`, `rel_improvement_rmse_h3`	number	fraction	1 − rmse_model / rmse_naive
`feasibility`	string	—	"complete", "partial" or "infeasible"
`feasibility_reason`	string or null	—	explanation if not complete
`data_issues`	array of {"issue","handling"}	—	data problems detected and how handled (empty list if none)
`n_input_rows`	object	—	{"canada_gdp_employment.csv": number of data rows read, excluding the header}
`n_analysed`	integer	—	observations in the last estimation sample
`random_seed`	integer or null	—	seed used, if any

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 and out/forecasts.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