| D1 |
WP22 used read-only; Task B predictors = WP22 real-time vintages (smoothed mode at each publication date), target = WP22 final smoothed index change |
protocol §7.3; WP22 vintage database records both modes |
| D2 |
Task A training pairs carry a random horizon h ∈ {1,3,6,12} with market/neighbour features dated h months before the sale |
otherwise horizon dependence cannot be learned (first HSHN run failed: untrained horizon embeddings, MAE 1–4) |
| D3 |
Tree models (LightGBM/XGBoost) and PyTorch models run in separate processes (--family) |
libomp conflict on macOS: segmentation fault at backward or deadlock in the same process |
| D4 |
Quarterly origins for Task A (12), monthly origins with quarterly re-estimation for Task B (34–45 per horizon) |
cost; 12-month targets must exist (last origin Sept. 2025 for h=12) |
| D5 |
No hyper-parameter tuning on test origins; fixed a priori (Table tab_hyper) |
protocol §7.3 |
| D6 |
Giacomini–White reported only where ≥ 12 origins (Task B); DM with HAC(h−1) + HLN; MCS with T_max and stationary bootstrap on per-origin mean losses |
short samples; cross-sectional dependence removed by averaging within origins |
| D7 |
QREFA = NNLS stack of QANSS, BHPF, DSGFN, RAREF (LightGBM excluded from the stack because of D3) |
process isolation |
| D8 |
Simulation forecasters run with 10 replications (6 for the neural family), horizons 1/3/6 |
compute on the node (RAREF HMM and torch models are slow) |
| D9 |
BHPF is conjugate/EM rather than MCMC; VI/HMC not available (PyMC absent) and unnecessary for a Gaussian hierarchy |
exactness |
| D10 |
CMA boundaries unavailable; metropolitan comparisons not needed for forecasting tasks |
inherited from WP22 |