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WP40 RAID: Phase 5 — VAR(p), FEVD/GFEVD, Diebold–Yilmaz connectedness (rolling), Ledoit–Wolf; statsmodels references; registry/status

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 2, 2026) parent 8bc0ff7 Browse files

9 changed files +1,628 −14

modified RESEARCH_STATUS.md +7 −6
@@ -1,6 +1,6 @@
1 1 # RESEARCH_STATUS.md — RAID (UQO WP 40)
2 2
3 −Last update: 2026-11-02 (Phase 4 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending ·
3 +Last update: 2026-11-02 (Phase 5 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending ·
4 4 🧭 experimental · ⛔ blocked · ⏳ planned · ✖ not implemented (deliberately).
5 5
6 6 ## Phases
@@ -11,7 +11,7 @@ Last update: 2026-11-02 (Phase 4 passed). Legend: ✅ verified by tests/artifact
11 11 | 2 Data | offline synthetic tests + opt-in live tests | ✅ passed 2026-11-02 | offline `[findata]` 4/4, live `[live]` 2/2 (60 assertions), `docs/operations/findata.md` |
12 12 | 3 Numerical primitives | accuracy + measured break-even | ✅ passed 2026-11-02 | `[metal]` 6 cases / 6 411 assertions; `experiments/results/bench_primitives_summary.json` (Metal wins: fused realized ≥ 39 k elements up to 3.4×, GARCH NLL P=64 up to 7.9×; CPU wins: rolling sums, GARCH P=1, small panels) |
13 13 | 4 Econometrics (Tier A) | references + Monte Carlo | ✅ passed 2026-11-02 | R (highfrequency/rugarch) + statsmodels/arch references, `tests/simulation` Monte Carlo, `docs/methods/registry.md` |
14 −| 5 Extensions (Tier B) | per-feature specs + tests | ⏳ | — |
14 +| 5 Extensions (Tier B) | per-feature specs + tests | ✅ passed 2026-11-02 (VAR/GFEVD/DY/LW only) | `tests/reference/test_var_reference.cpp` 184 assertions vs statsmodels |
15 15 | 6 Benchmarking E1–E7 | every claim traceable | ⏳ | — |
16 16 | 7 Manuscript | reproducible build, no placeholders | ⏳ | — |
17 17 | 8 Independent review | clean-checkout reproduction | ⏳ | — |
@@ -25,8 +25,8 @@ Last update: 2026-11-02 (Phase 4 passed). Legend: ✅ verified by tests/artifact
25 25 | HAR-RV, log-HAR, HAR-X, HARQ, rolling, batched | A | ✅ (highfrequency 1e−8, statsmodels 1e−9) | ✅ batched sufficient statistics | highfrequency 1.0.3, statsmodels 0.15 | ✅ |
26 26 | GARCH(1,1), GJR, EGARCH (N, t), QMLE/Hessian/OPG SE, forecasts | A | ✅ (arch 8.0: loglik 5e−4, params 5 %) | ✅ batched NLL 2e−5 (grids) | arch 8.0 ✅, rugarch 1.5.6 (Phase 4) | ✅ CPU |
27 27 | Bootstrap (stationary / circular / iid), Monte Carlo harness | A | ✅ (theory + MC coverage) | ⏳ GPU replicas (E5) | theory (Politis–Romano) | ✅ |
28 −| VAR, GFEVD, Diebold–Yilmaz | B | ⏳ | ⏳ | statsmodels VAR | ⏳ |
29 −| Realized GARCH, HEAVY, DCC, PCA/shrinkage, Kalman | B | ✖ until Tier A passes | | | ⏳ |
28 +| VAR(p), FEVD, GFEVD, Diebold–Yilmaz (static, rolling), Ledoit–Wolf | B | ✅ (statsmodels 1e−9) | ⏳ (E4: batched covariance inputs) | statsmodels 0.15 | ✅ |
29 +| Realized GARCH, HEAVY, DCC, GARCH-MIDAS, FIGARCH, BVAR/VECM, Kalman, change-point | B | ✖ not implemented (deliberate: no experiment in E1–E7 needs them) | ✖ | — | ✖ |
30 30 | Pre-averaging, realized kernels, MSRV, BNS test | C | ✖ (1-min bars) | | highfrequency | 🧭 only if validated |
31 31 | Hawkes, order-flow, LOB methods | C | ✖ | ✖ | — | ✖ no admissible data (bars only) |
32 32
@@ -35,5 +35,6 @@ Last update: 2026-11-02 (Phase 4 passed). Legend: ✅ verified by tests/artifact
35 35 not tested unless they come back.
36 36
37 37 ## Next safe action
38 −Phase 5 (Tier B, selected for research value): VAR(p) + generalized FEVD + Diebold–Yilmaz connectedness (rolling, batched
39 −realized-covariance inputs) for E4; shrinkage covariance; then Phase 6 experiments E1–E7.
38 +Phase 6: experiments E1–E7 (configs in `experiments/configs/`, drivers in `benchmarks/` and `experiments/scripts/`,
39 +raw records + manifests under `experiments/results/`), starting with the E7 bundle for cluster nodes and the FinData
40 +universe/export for E1–E4.
modified datasets/fixtures/reference_values.json +189 −8
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1200 +1.6136825314296015,-0.28742571544311191,-0.18740127851410754,0.65287483630130261
1201 +0.37134591233596825,0.1676733479696802,0.53320721038466135,1.5297757640836296
modified docs/PROGRESS.md +8 −0
@@ -149,3 +149,11 @@ Executed:
149 149 - Full suite: `ctest` all green (see summary in RESEARCH_STATUS).
150 150
151 151 Gate evidence: reference tables (R + Python), Monte Carlo logs, registry entries with tolerances.
152 +
153 +## Phase 5 — Extensions, Tier B (2026-11-02, 17:40–18:10 ET) — GATE PASSED
154 +Selection by research value (E4 needs them): VAR(p), orthogonalised and generalized FEVD, Diebold–Yilmaz connectedness
155 +(static + rolling), Ledoit–Wolf shrinkage. `models/var.{hpp,cpp}`. Reference: statsmodels VAR on a simulated VAR(2)
156 +panel (`datasets/fixtures/var_panel.csv`): coefficients/Σ_u/llf/orthogonalised FEVD 1e−9, generalized FEVD (hand
157 +computation in Python) 1e−9, DY totals 1e−7, BIC order, LW shrinkage 1e−9 (`tests/reference/test_var_reference.cpp`,
158 +184 assertions). Rolling connectedness parallel over windows (0 failures on the fixture). Other Tier B/C items are
159 +deliberately not implemented (registry).
modified docs/methods/registry.md +13 −0
@@ -35,6 +35,19 @@ reference comparison pending · 🧭 experimental · ✖ not implemented. "Toler
35 35 | `bootstrap.stationary` / `circular` / `iid` | Politis–Romano 1994 (geometric blocks, mean length b or n^{1/3}) / circular blocks / iid (comparison only); replica r ← Rng(seed, r); percentile CI, SE, bias; `bootstrap_rows` for regressions; `bootstrap_rv` | series | theory: AR(1) long-run SE (block SE within 35 %, > 1.6× iid SE); MC coverage 0.89 (R = 100) | as stated | ✅ | ⏳ (GPU replicas, E5) | ✅ |
36 36 | `simulation.*` | Monte Carlo: GARCH(1,1) n=2000 R=120 — coverage 0.90–0.95, |bias| ≤ 0.003, no failures; HAR h=5 HAC coverage 0.895–0.945 (R=200, T=1200, pseudo-true from T=200 000); RV RMSE within 10 % of √(2/M)·IV; NLL finite-difference gradients stable across h | — | `tests/simulation` | — | ✅ | — | ✅ |
37 37
38 +## Tier B (selected, validated 2026-11-02)
39 +| Key | Definition | Inputs | Reference & tolerance | CPU | Metal | Status |
40 +|---|---|---|---|---|---|---|
41 +| `var.p` | VAR(p) with intercept by multivariate LS (QR); Σ_u with ddof = 1+Np; Gaussian log-likelihood; companion-root stability; AIC/BIC/HQ order selection on the common sample | T×N panel | statsmodels 0.15 `VAR.fit(p, trend="c")`: params 1e−9, Σ_u 1e−9, llf 1e−9, `select_order` BIC | ✅ | — | ✅ |
42 +| `var.fevd` | orthogonalised (Cholesky) FEVD, horizon H | VarFit | statsmodels `fevd(H)` 1e−9 | ✅ | — | ✅ |
43 +| `var.gfevd` | generalized FEVD (Koop–Pesaran–Potter 1996; Pesaran–Shin 1998), rows normalised to one (Diebold–Yilmaz 2012) | VarFit | hand-computed Python from statsmodels Φ_h, Σ_u: 1e−9 | ✅ | — | ✅ |
44 +| `connectedness.dy` | total = 100·Σ_{i≠j}θ̃_{ij}/N, from/to/net; rolling windows (parallel over windows) | gfevd | same reference 1e−7 | ✅ | ⏳ (batched covariance inputs, E4) | ✅ |
45 +| `cov.ledoit_wolf` | Σ* = (1−δ)S + δμI with the analytical δ* (Ledoit–Wolf 2004) | n×p | sklearn `ledoit_wolf` formula (hand-computed): 1e−9 | ✅ | — | ✅ |
46 +
47 +Not implemented (Tier B/C, by decision — research value for this paper insufficient or data inadmissible): Realized GARCH,
48 +HEAVY, GARCH-MIDAS, FIGARCH, DCC, BVAR/VECM, local projections, Kalman, change-point, pre-averaging, realized kernels,
49 +MSRV, BNS test, Hawkes, order-flow. Listed in RESEARCH_STATUS as ✖/⏳ with the reason.
50 +
38 51 Conventions that differ from references and are **documented, not hidden**:
39 52 1. GARCH initial variance: RAID uses the sample variance of ε; `arch` uses a backcast (exponentially weighted) and
40 53 `rugarch` the sample variance by default — hence the 5e−4 log-likelihood tolerance. A `backcast` option is planned so
added include/raid/models/var.hpp +51 −0
@@ -0,0 +1,51 @@
1 +// VAR(p), generalized FEVD (Pesaran–Shin 1998, Koop–Pesaran–Potter 1996) and Diebold–Yilmaz (2012, 2014) connectedness.
2 +// y_t = c + A_1 y_{t−1} + … + A_p y_{t−p} + u_t, Σ = E[u u'] (OLS equation by equation = multivariate LS)
3 +// MA representation: y_t = Σ_h Φ_h u_{t−h}, Φ_0 = I, Φ_h = Σ_{j=1}^{min(h,p)} A_j Φ_{h−j}
4 +// GFEVD (horizon H): θ_{ij}(H) = σ_jj^{-1} Σ_{h=0}^{H−1} (e_i' Φ_h Σ e_j)² / Σ_{h=0}^{H−1} e_i' Φ_h Σ Φ_h' e_i,
5 +// normalised row-wise: θ̃_{ij} = θ_{ij} / Σ_j θ_{ij} (Diebold–Yilmaz 2012 eq. 3–4)
6 +// Connectedness: total C = 100 · Σ_{i≠j} θ̃_{ij} / N ; from-others C_i← = Σ_{j≠i} θ̃_{ij} ; to-others C_→j = Σ_{i≠j} θ̃_{ij} ; net = to − from.
7 +#pragma once
8 +#include <vector>
9 +#include <Eigen/Dense>
10 +#include "raid/core/types.hpp"
11 +#include "raid/core/estimate.hpp"
12 +
13 +namespace raid::models {
14 +
15 +struct VarFit {
16 + int p{1}; usize n_obs{0}; int N{0};
17 + Eigen::MatrixXd coef; // N × (1 + N p): [c, A_1, …, A_p]
18 + Eigen::MatrixXd sigma; // residual covariance (ddof = N p + 1, as statsmodels VAR `sigma_u`)
19 + Eigen::MatrixXd sigma_mle; // ddof = 0
20 + Eigen::MatrixXd residuals; // n_obs × N
21 + f64 loglik{0}, aic{0}, bic{0}, hqic{0};
22 + bool stable{true}; // all companion eigenvalues inside the unit circle
23 + f64 max_root{0};
24 + f64 rcond{0};
25 + std::vector<std::string> warnings;
26 +};
27 +/// Y is T × N (rows = time). Fits by OLS with an intercept (as statsmodels `VAR(Y).fit(p, trend="c")`).
28 +VarFit var_fit(const Eigen::Ref<const Eigen::MatrixXd>& Y, int p);
29 +/// Information criteria for p = 1..pmax on the common sample (observations pmax+1..T), as statsmodels `select_order`.
30 +struct VarOrder { std::vector<int> p; std::vector<f64> aic, bic, hqic; int aic_best, bic_best, hqic_best; };
31 +VarOrder var_select_order(const Eigen::Ref<const Eigen::MatrixXd>& Y, int pmax);
32 +/// MA coefficient matrices Φ_0..Φ_{H−1}.
33 +std::vector<Eigen::MatrixXd> var_ma(const VarFit& f, int H);
34 +/// Generalized FEVD at horizon H (N × N, rows normalised to one).
35 +Eigen::MatrixXd gfevd(const VarFit& f, int H);
36 +/// Orthogonalised (Cholesky) FEVD at horizon H, for comparison with statsmodels `fevd`.
37 +Eigen::MatrixXd fevd_cholesky(const VarFit& f, int H);
38 +
39 +struct Connectedness { Eigen::MatrixXd table; f64 total; Eigen::VectorXd from, to, net; int H; };
40 +Connectedness connectedness(const Eigen::MatrixXd& gfevd_table);
41 +Connectedness connectedness(const VarFit& f, int H);
42 +
43 +/// Rolling connectedness: windows of `window` rows, step `step`; one VAR(p) per window (CPU, parallel over windows).
44 +struct RollingConnectedness { std::vector<usize> end_index; std::vector<f64> total; Eigen::MatrixXd net; usize n_windows{0}; usize failures{0}; f64 seconds{0}; };
45 +RollingConnectedness rolling_connectedness(const Eigen::Ref<const Eigen::MatrixXd>& Y, int p, int H, usize window, usize step = 1);
46 +
47 +/// Ledoit–Wolf (2004) linear shrinkage toward the scaled identity: Σ* = (1−δ) S + δ μ I, with the analytical δ*.
48 +struct ShrunkCovariance { Eigen::MatrixXd cov; f64 shrinkage; f64 mu; };
49 +ShrunkCovariance ledoit_wolf(const Eigen::Ref<const Eigen::MatrixXd>& X, bool assume_centered = false);
50 +
51 +} // namespace raid::models
modified scripts/make_fixtures.py +35 −0
@@ -131,3 +131,38 @@ for name, kw in [("h1", dict(h=1)), ("h5", dict(h=5)), ("h22", dict(h=22)), ("lo
131 131 har[name] = {"params": f.params.tolist(), "hac_bse": f.bse.tolist(), "hac_lags": L, "n": n, "ssr": float(f.ssr), "rsquared": float(f.rsquared)}
132 132 ref["har"] = har; json.dump(ref, open(os.path.join(out_dir, "reference_values.json"), "w"), indent=1)
133 133 print("HAR references:", {k: v["n"] for k, v in har.items()})
134 +
135 +# --- VAR(2) reference: statsmodels VAR, orthogonalised FEVD, generalized FEVD (Pesaran–Shin) by hand, DY totals, LW ----
136 +from statsmodels.tsa.api import VAR
137 +g = np.random.default_rng(77); N = 4; T = 1200
138 +A1 = np.array([[0.5, 0.1, 0.0, 0.05], [0.0, 0.4, 0.15, 0.0], [0.1, 0.0, 0.3, 0.1], [0.05, 0.05, 0.0, 0.45]]); A2 = 0.15 * np.eye(N)
139 +L = np.linalg.cholesky(np.array([[1.0, 0.3, 0.1, 0.0], [0.3, 1.0, 0.2, 0.1], [0.1, 0.2, 1.0, 0.3], [0.0, 0.1, 0.3, 1.0]]))
140 +Y = np.zeros((T, N))
141 +for t in range(2, T): Y[t] = 0.1 + A1 @ Y[t - 1] + A2 @ Y[t - 2] + L @ g.standard_normal(N)
142 +np.savetxt(os.path.join(out_dir, "var_panel.csv"), Y, delimiter=",", header=",".join(f"y{i}" for i in range(N)), comments="", fmt="%.17g")
143 +res = VAR(Y).fit(2, trend="c"); H = 10
144 +Phi = [np.eye(N)]
145 +coefs = [res.coefs[i] for i in range(2)]
146 +for h in range(1, H): Phi.append(sum(coefs[j - 1] @ Phi[h - j] for j in range(1, min(h, 2) + 1)))
147 +S = res.sigma_u
148 +num = np.zeros((N, N)); den = np.zeros(N)
149 +for h in range(H):
150 + PS = Phi[h] @ S
151 + for i in range(N):
152 + den[i] += PS[i] @ Phi[h][i]
153 + for j in range(N): num[i, j] += PS[i, j] ** 2 / S[j, j]
154 +th = num / den[:, None]; th = th / th.sum(axis=1, keepdims=True)
155 +dy_total = 100 * (th.sum() - np.trace(th)) / N; dy_from = 100 * (th.sum(axis=1) - np.diag(th)); dy_to = 100 * (th.sum(axis=0) - np.diag(th))
156 +sel = VAR(Y).select_order(4)
157 +try:
158 + from sklearn.covariance import ledoit_wolf as sk_lw
159 + lw_cov, lw_s = sk_lw(Y)
160 +except Exception:
161 + Xc = Y - Y.mean(axis=0); n = Xc.shape[0]; Sc = Xc.T @ Xc / n; mu = np.trace(Sc) / N
162 + d2 = np.sum((Sc - mu * np.eye(N)) ** 2) / N; b = sum(np.sum((np.outer(x, x) - Sc) ** 2) for x in Xc) / (n * n * N); lw_s = min(1.0, b / d2); lw_cov = (1 - lw_s) * Sc + lw_s * mu * np.eye(N)
163 +ref = json.load(open(os.path.join(out_dir, "reference_values.json")))
164 +ref["var"] = {"nobs": int(res.nobs), "params": res.params.tolist(), "sigma_u": res.sigma_u.tolist(), "llf": float(res.llf), "fevd10": res.fevd(H).decomp[:, -1, :].tolist(),
165 + "gfevd10": th.tolist(), "dy_total10": float(dy_total), "dy_from10": dy_from.tolist(), "dy_to10": dy_to.tolist(), "bic_order": int(sel.bic), "aic_order": int(sel.aic),
166 + "lw_shrinkage": float(lw_s), "lw_cov": np.asarray(lw_cov).tolist(), "statsmodels_version": sm.__version__}
167 +json.dump(ref, open(os.path.join(out_dir, "reference_values.json"), "w"), indent=1)
168 +print("VAR reference: nobs", res.nobs, "DY total", round(dy_total, 3), "bic order", int(sel.bic), "LW shrinkage", round(float(lw_s), 4))
added src/models/var.cpp +98 −0
@@ -0,0 +1,98 @@
1 +#include "raid/models/var.hpp"
2 +#include "raid/core/error.hpp"
3 +#include "raid/core/thread_pool.hpp"
4 +#include <Eigen/Eigenvalues>
5 +#include <chrono>
6 +#include <cmath>
7 +#include <numbers>
8 +
9 +namespace raid::models {
10 +
11 +namespace {
12 +void build_design(const Eigen::Ref<const Eigen::MatrixXd>& Y, int p, usize start, Eigen::MatrixXd& X, Eigen::MatrixXd& Yt) {
13 + const Eigen::Index T = Y.rows(), N = Y.cols(); const Eigen::Index n = T - static_cast<Eigen::Index>(start);
14 + X.resize(n, 1 + N * p); Yt.resize(n, N);
15 + for (Eigen::Index i = 0; i < n; ++i) { const Eigen::Index t = static_cast<Eigen::Index>(start) + i; X(i, 0) = 1.0; for (int l = 1; l <= p; ++l) X.block(i, 1 + (l - 1) * N, 1, N) = Y.row(t - l); Yt.row(i) = Y.row(t); }
16 +}
17 +}
18 +
19 +VarFit var_fit(const Eigen::Ref<const Eigen::MatrixXd>& Y, int p) {
20 + const Eigen::Index T = Y.rows(), N = Y.cols(); RAID_REQUIRE(p >= 1 && T > p + N * p + 2, insufficient_samples, "var_fit: need T > p + N·p + 2");
21 + RAID_REQUIRE(Y.allFinite(), data_quality, "var_fit: non-finite values");
22 + VarFit f; f.p = p; f.N = static_cast<int>(N); Eigen::MatrixXd X, Yt; build_design(Y, p, static_cast<usize>(p), X, Yt); f.n_obs = static_cast<usize>(X.rows());
23 + Eigen::ColPivHouseholderQR<Eigen::MatrixXd> qr(X); qr.setThreshold(1e-12); const Eigen::Index k = X.cols();
24 + const Eigen::MatrixXd R = qr.matrixR().topLeftCorner(k, k).template triangularView<Eigen::Upper>(); f.rcond = std::abs(R(k - 1, k - 1)) / std::abs(R(0, 0));
25 + if (qr.rank() < k) f.warnings.push_back("VAR design rank deficient (rank " + std::to_string(qr.rank()) + " < " + std::to_string(k) + ")");
26 + Eigen::MatrixXd B = qr.solve(Yt); // k × N
27 + f.coef = B.transpose(); f.residuals = Yt - X * B; const f64 n = static_cast<f64>(f.n_obs);
28 + f.sigma_mle = f.residuals.transpose() * f.residuals / n; f.sigma = f.residuals.transpose() * f.residuals / (n - static_cast<f64>(k));
29 + // log-likelihood (Gaussian, MLE covariance), as statsmodels VARResults.llf
30 + const f64 ld = std::log(f.sigma_mle.determinant()); const f64 Nd = static_cast<f64>(N);
31 + f.loglik = -0.5 * n * (Nd * std::log(2 * std::numbers::pi) + ld + Nd);
32 + const f64 nparams = static_cast<f64>(k) * Nd; // statsmodels: ln|Σ_mle| + 2 k N / n etc. (uses sigma with nobs correction: see select_order); we report the mle-based criteria
33 + f.aic = ld + 2.0 * nparams / n; f.bic = ld + std::log(n) * nparams / n; f.hqic = ld + 2.0 * std::log(std::log(n)) * nparams / n;
34 + // stability: companion matrix eigenvalues
35 + Eigen::MatrixXd C = Eigen::MatrixXd::Zero(N * p, N * p); for (int l = 1; l <= p; ++l) C.block(0, (l - 1) * N, N, N) = f.coef.block(0, 1 + (l - 1) * N, N, N); if (p > 1) C.block(N, 0, N * (p - 1), N * (p - 1)) = Eigen::MatrixXd::Identity(N * (p - 1), N * (p - 1));
36 + Eigen::EigenSolver<Eigen::MatrixXd> es(C, false); f.max_root = es.eigenvalues().cwiseAbs().maxCoeff(); f.stable = f.max_root < 1.0; if (!f.stable) f.warnings.push_back("VAR not stable (max root " + std::to_string(f.max_root) + ")");
37 + return f;
38 +}
39 +
40 +VarOrder var_select_order(const Eigen::Ref<const Eigen::MatrixXd>& Y, int pmax) {
41 + VarOrder o; const Eigen::Index N = Y.cols(); const f64 Nd = static_cast<f64>(N);
42 + for (int p = 1; p <= pmax; ++p) { Eigen::MatrixXd X, Yt; build_design(Y, p, static_cast<usize>(pmax), X, Yt); Eigen::ColPivHouseholderQR<Eigen::MatrixXd> qr(X); Eigen::MatrixXd B = qr.solve(Yt); Eigen::MatrixXd U = Yt - X * B; const f64 n = static_cast<f64>(X.rows()); const f64 ld = std::log((U.transpose() * U / n).determinant()); const f64 np = static_cast<f64>(X.cols()) * Nd;
43 + o.p.push_back(p); o.aic.push_back(ld + 2 * np / n); o.bic.push_back(ld + std::log(n) * np / n); o.hqic.push_back(ld + 2 * std::log(std::log(n)) * np / n); }
44 + auto best = [&](const std::vector<f64>& v) { int b = 0; for (usize i = 1; i < v.size(); ++i) if (v[i] < v[static_cast<usize>(b)]) b = static_cast<int>(i); return o.p[static_cast<usize>(b)]; };
45 + o.aic_best = best(o.aic); o.bic_best = best(o.bic); o.hqic_best = best(o.hqic); return o;
46 +}
47 +
48 +std::vector<Eigen::MatrixXd> var_ma(const VarFit& f, int H) {
49 + const int N = f.N; std::vector<Eigen::MatrixXd> Phi(static_cast<usize>(H)); Phi[0] = Eigen::MatrixXd::Identity(N, N);
50 + for (int h = 1; h < H; ++h) { Phi[static_cast<usize>(h)] = Eigen::MatrixXd::Zero(N, N); for (int j = 1; j <= std::min(h, f.p); ++j) Phi[static_cast<usize>(h)] += f.coef.block(0, 1 + (j - 1) * N, N, N) * Phi[static_cast<usize>(h - j)]; }
51 + return Phi;
52 +}
53 +
54 +Eigen::MatrixXd gfevd(const VarFit& f, int H) {
55 + RAID_REQUIRE(H >= 1, invalid_argument, "gfevd: H >= 1"); const int N = f.N; auto Phi = var_ma(f, H); const Eigen::MatrixXd& S = f.sigma;
56 + Eigen::MatrixXd num = Eigen::MatrixXd::Zero(N, N); Eigen::VectorXd den = Eigen::VectorXd::Zero(N);
57 + for (int h = 0; h < H; ++h) { const Eigen::MatrixXd PS = Phi[static_cast<usize>(h)] * S; for (int i = 0; i < N; ++i) { den[i] += (PS.row(i) * Phi[static_cast<usize>(h)].row(i).transpose())(0, 0); for (int j = 0; j < N; ++j) num(i, j) += PS(i, j) * PS(i, j) / S(j, j); } }
58 + Eigen::MatrixXd th(N, N); for (int i = 0; i < N; ++i) for (int j = 0; j < N; ++j) th(i, j) = num(i, j) / den[i];
59 + for (int i = 0; i < N; ++i) th.row(i) /= th.row(i).sum();
60 + return th;
61 +}
62 +
63 +Eigen::MatrixXd fevd_cholesky(const VarFit& f, int H) {
64 + const int N = f.N; auto Phi = var_ma(f, H); Eigen::LLT<Eigen::MatrixXd> llt(f.sigma); RAID_REQUIRE(llt.info() == Eigen::Success, numerical, "fevd: Σ not PD"); const Eigen::MatrixXd P = llt.matrixL();
65 + Eigen::MatrixXd num = Eigen::MatrixXd::Zero(N, N); Eigen::VectorXd den = Eigen::VectorXd::Zero(N);
66 + for (int h = 0; h < H; ++h) { const Eigen::MatrixXd Th = Phi[static_cast<usize>(h)] * P; for (int i = 0; i < N; ++i) for (int j = 0; j < N; ++j) { num(i, j) += Th(i, j) * Th(i, j); den[i] += Th(i, j) * Th(i, j); } }
67 + Eigen::MatrixXd out(N, N); for (int i = 0; i < N; ++i) for (int j = 0; j < N; ++j) out(i, j) = num(i, j) / den[i]; return out;
68 +}
69 +
70 +Connectedness connectedness(const Eigen::MatrixXd& th) {
71 + const Eigen::Index N = th.rows(); Connectedness c; c.table = th; c.from.resize(N); c.to.resize(N); c.net.resize(N); f64 off = 0;
72 + for (Eigen::Index i = 0; i < N; ++i) { f64 fr = 0, to = 0; for (Eigen::Index j = 0; j < N; ++j) if (i != j) { fr += th(i, j); to += th(j, i); off += th(i, j); } c.from[i] = 100.0 * fr; c.to[i] = 100.0 * to; c.net[i] = c.to[i] - c.from[i]; }
73 + c.total = 100.0 * off / static_cast<f64>(N); c.H = 0; return c;
74 +}
75 +Connectedness connectedness(const VarFit& f, int H) { auto c = connectedness(gfevd(f, H)); c.H = H; return c; }
76 +
77 +RollingConnectedness rolling_connectedness(const Eigen::Ref<const Eigen::MatrixXd>& Y, int p, int H, usize window, usize step) {
78 + const auto t0 = std::chrono::steady_clock::now(); RollingConnectedness r; const usize T = static_cast<usize>(Y.rows()), N = static_cast<usize>(Y.cols());
79 + RAID_REQUIRE(window <= T && window > static_cast<usize>(p + static_cast<int>(N) * p + 10), insufficient_samples, "rolling_connectedness: window");
80 + const usize nw = (T - window) / step + 1; r.end_index.resize(nw); r.total.assign(nw, std::numeric_limits<f64>::quiet_NaN()); r.net.resize(static_cast<Eigen::Index>(N), static_cast<Eigen::Index>(nw)); r.net.setConstant(std::numeric_limits<f64>::quiet_NaN()); std::atomic<usize> fails{0};
81 + Eigen::MatrixXd Yc = Y;
82 + ThreadPool::global().parallel_for(nw, [&](usize a, usize b) { for (usize w = a; w < b; ++w) { const usize beg = w * step; r.end_index[w] = beg + window; try { auto f = var_fit(Yc.middleRows(static_cast<Eigen::Index>(beg), static_cast<Eigen::Index>(window)), p); auto c = connectedness(f, H); r.total[w] = c.total; r.net.col(static_cast<Eigen::Index>(w)) = c.net; } catch (const RaidError&) { ++fails; } } });
83 + r.n_windows = nw; r.failures = fails.load(); r.seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count(); return r;
84 +}
85 +
86 +ShrunkCovariance ledoit_wolf(const Eigen::Ref<const Eigen::MatrixXd>& X0, bool assume_centered) {
87 + // Ledoit & Wolf (2004) "A well-conditioned estimator for large-dimensional covariance matrices", as sklearn.covariance.ledoit_wolf
88 + const Eigen::Index n = X0.rows(), p = X0.cols(); RAID_REQUIRE(n >= 2, insufficient_samples, "ledoit_wolf: n >= 2");
89 + Eigen::MatrixXd X = X0; if (!assume_centered) X.rowwise() -= X0.colwise().mean();
90 + const Eigen::MatrixXd S = X.transpose() * X / static_cast<f64>(n); const f64 mu = S.trace() / static_cast<f64>(p);
91 + // δ² = ||S − μI||²_F / p ; β̄ = (1/n²) Σ_i ||x_i x_i' − S||²_F / p (sklearn formula); shrinkage = min(β̄/δ², 1)
92 + const f64 delta2 = (S - mu * Eigen::MatrixXd::Identity(p, p)).squaredNorm() / static_cast<f64>(p);
93 + f64 beta = 0; for (Eigen::Index i = 0; i < n; ++i) { const Eigen::MatrixXd xx = X.row(i).transpose() * X.row(i); beta += (xx - S).squaredNorm(); } beta /= static_cast<f64>(n) * static_cast<f64>(n) * static_cast<f64>(p);
94 + const f64 shrink = delta2 > 0 ? std::min(1.0, beta / delta2) : 0.0;
95 + ShrunkCovariance out; out.shrinkage = shrink; out.mu = mu; out.cov = (1 - shrink) * S + shrink * mu * Eigen::MatrixXd::Identity(p, p); return out;
96 +}
97 +
98 +} // namespace raid::models
added tests/reference/test_var_reference.cpp +26 −0
@@ -0,0 +1,26 @@
1 +#include <catch2/catch_test_macros.hpp>
2 +#include <catch2/matchers/catch_matchers_floating_point.hpp>
3 +#include <nlohmann/json.hpp>
4 +#include <fstream>
5 +#include "raid/models/var.hpp"
6 +#include "raid/core/rng.hpp"
7 +using namespace raid; using json = nlohmann::json; using Catch::Matchers::WithinRel; using Catch::Matchers::WithinAbs;
8 +
9 +static Eigen::MatrixXd load_var_csv(int& N) { std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/var_panel.csv"); REQUIRE(f.good()); std::string line; std::getline(f, line); std::vector<std::vector<double>> rows; while (std::getline(f, line)) { std::vector<double> r; usize s = 0; for (usize i = 0; i <= line.size(); ++i) if (i == line.size() || line[i] == ',') { r.push_back(std::stod(line.substr(s, i - s))); s = i + 1; } rows.push_back(r); } N = static_cast<int>(rows[0].size()); Eigen::MatrixXd Y(rows.size(), N); for (usize i = 0; i < rows.size(); ++i) for (int j = 0; j < N; ++j) Y(static_cast<Eigen::Index>(i), j) = rows[i][static_cast<usize>(j)]; return Y; }
10 +
11 +TEST_CASE("VAR(2) coefficients, Σ, FEVD and generalized FEVD match statsmodels", "[reference][var]") {
12 + std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/reference_values.json"); json ref; f >> ref; auto v = ref["var"]; int N; auto Y = load_var_csv(N);
13 + auto fit = models::var_fit(Y, 2); CHECK(fit.n_obs == v["nobs"].get<usize>()); CHECK(fit.stable);
14 + // statsmodels params: (1 + N p) × N, rows = [const, L1.y1..L1.yN, L2...], our coef is N × (1+Np)
15 + for (int i = 0; i < N; ++i) for (int c = 0; c < 1 + 2 * N; ++c) CHECK_THAT(fit.coef(i, c), WithinAbs(v["params"][c][i].get<double>(), 1e-9));
16 + for (int i = 0; i < N; ++i) for (int j = 0; j < N; ++j) CHECK_THAT(fit.sigma(i, j), WithinRel(v["sigma_u"][i][j].get<double>(), 1e-9));
17 + CHECK_THAT(fit.loglik, WithinRel(v["llf"].get<double>(), 1e-9));
18 + auto fo = models::fevd_cholesky(fit, 10); for (int i = 0; i < N; ++i) for (int j = 0; j < N; ++j) CHECK_THAT(fo(i, j), WithinAbs(v["fevd10"][i][j].get<double>(), 1e-9));
19 + auto g = models::gfevd(fit, 10); for (int i = 0; i < N; ++i) { double rs = 0; for (int j = 0; j < N; ++j) { rs += g(i, j); CHECK_THAT(g(i, j), WithinAbs(v["gfevd10"][i][j].get<double>(), 1e-9)); } CHECK_THAT(rs, WithinAbs(1.0, 1e-12)); }
20 + auto c = models::connectedness(fit, 10); CHECK_THAT(c.total, WithinAbs(v["dy_total10"].get<double>(), 1e-7)); for (int i = 0; i < N; ++i) { CHECK_THAT(c.from[i], WithinAbs(v["dy_from10"][i].get<double>(), 1e-7)); CHECK_THAT(c.to[i], WithinAbs(v["dy_to10"][i].get<double>(), 1e-7)); }
21 + auto ord = models::var_select_order(Y, 4); CHECK(ord.bic_best == v["bic_order"].get<int>());
22 + auto lw = models::ledoit_wolf(Y); CHECK_THAT(lw.shrinkage, WithinAbs(v["lw_shrinkage"].get<double>(), 1e-9)); for (int i = 0; i < N; ++i) CHECK_THAT(lw.cov(i, i), WithinRel(v["lw_cov"][i][i].get<double>(), 1e-9));
23 +}
24 +TEST_CASE("Rolling connectedness runs in parallel and totals are bounded", "[var]") {
25 + int N; auto Y = load_var_csv(N); auto r = models::rolling_connectedness(Y, 2, 10, 300, 25); CHECK(r.n_windows > 5); CHECK(r.failures == 0); for (double t : r.total) { CHECK(t >= 0); CHECK(t <= 100); }
26 +}
27
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