What the model found
Fees and volume move together in this sample.
The model estimates a positive relationship between their daily log changes. Because fees are constructed from volume, this mostly describes the fee mechanism. It does not answer whether Intents creates sustainable value for NEAR.
Estimated relationship · slope
0.8542
95% confidence interval · 0.5719–1.1366
What this supports
A short-run association between the two reported series over the fitted observations.
What remains unanswered
Protocol revenue, sustainable fee income and value captured by the NEAR token.
The recorded interpretation (verbatim)
{
"sentences": [
"The fitted slope on 'dlog(provider_intents_volume_usd|SRC-LLAMA)' is 0.8542493147420771 (HAC standard error 0.14203451836578282, 95% CI [0.5719403297667323, 1.136558299717422]): a short-run association between contemporaneous transformed values of the declared series; it is not a causal effect and not an elasticity of demand.",
"The estimate uses 89 observations between 2026-08-07 and 2026-11-03; a window of 90 calendar days cannot establish a long-term stable mechanism.",
"The ADF unit-root null is not rejected for at least one level series; the level is treated as nonstationary and the preregistered difference baseline stays the published form.",
"The KPSS stationarity null is not rejected for any level series; short samples make this weak evidence, not proof of stationarity.",
"Ljung-Box does not reject the no-autocorrelation null at the declared lags; with this sample size that is weak evidence, not proof of a correct specification.",
"Breusch-Pagan indicates heteroskedasticity; the preregistered response is the already-declared HAC covariance — no reweighting, no outlier removal and no specification change was performed.",
"Influence check: 4 observation(s) exceed the preregistered Cook's distance threshold (0.0449438202247191); they are reported with dates and remain in the sample — removal requires an operational reason and a with/without report.",
"No protocol-revenue, sustainable-revenue, token-valuation, causal or demand-elasticity claim follows from this output: the fitted object is the named proxy association only, and the mechanical fee-on-volume construction means the slope largely restates the effective fee rate."
],
"template_id": "bounded_association_v1",
"authoring_note": "this is the bounded template default rendered by the diagnostics run; an authored operator interpretation, if any, is added at publication time (NI-054) and never replaces the attached limitations.",
"gate_limit_codes": [
"MECHANICAL_FEE_VOLUME_RELATIONSHIP",
"PROXY_NOT_PROTOCOL_REVENUE",
"OQ02_JUDGING_ACCEPTANCE_RISK",
"SOURCE_REVISIONS_POSSIBLE",
"SINGLE_PROVIDER_LINEAGE_NOT_INDEPENDENT",
"SHORT_SAMPLE_NO_LONG_RUN_CLAIM",
"NO_CAUSAL_IDENTIFICATION"
],
"rendering_policy": "every sentence below is rendered from a fixed template in research/models/diagnostics.py with recorded numbers and recorded test decisions substituted; there is no free-form generation, no causal wording and no sustainability wording anywhere in this module.",
"prohibited_labels": [
"protocol_revenue",
"token_valuation",
"causal_effect",
"demand_elasticity",
"sustainable_revenue"
],
"spec_limitation_notes": [
"Mechanical construction warning (docs/ECONOMETRIC_METHODOLOGY.md): provider gross fees are reported as a fee applied to the same provider's reported volume, so a fee-on-volume association largely restates the effective fee rate and must be displayed before any coefficient interpretation.",
"Both variables share one provider lineage (Dune); the association is not independent confirmation of anything.",
"The output is the named proxy association only; it is never protocol revenue (OQ-01) and H1/H4 stay unresolved (OQ-02 judging risk)."
],
"mechanical_construction_warning": "Mechanical construction warning (docs/ECONOMETRIC_METHODOLOGY.md): provider gross fees are reported as a fee applied to the same provider's reported volume, so a fee-on-volume association largely restates the effective fee rate and must be displayed before any coefficient interpretation."
}Exact coefficients and inference
The summary above is rounded for display. The recorded values are preserved below, verbatim. No specification was selected using p-values.
| Term | Estimate | HAC standard error |
|---|---|---|
| const | -0.02769749207193234 | 0.03560372368015006 |
| dlog(provider_intents_volume_usd|SRC-LLAMA) | 0.8542493147420771 | 0.14203451836578282 |
| const · 95% CI | -0.0984637481140075 to 0.04306876397014282 | |
| dlog(provider_intents_volume_usd|SRC-LLAMA) · 95% CI | 0.5719403297667323 to 1.136558299717422 | |
| const · t / p | -0.77793806964563 / 0.43871601689821427 | |
| dlog(provider_intents_volume_usd|SRC-LLAMA) · t / p | 6.014378227003388 / 4.146222767879358e-8 | |
The fitted observations cover 6 Aug – 3 Nov 2026. The input window is [6 Aug 2026, 4 Nov 2026) UTC; one observation is lost to the daily difference.
All recorded limitations · 7
Prohibited labels — this result must never be presented as:
- protocol_revenue
- token_valuation
- causal_effect
- demand_elasticity
- sustainable_revenue
Recorded limitation notes (verbatim)
- Mechanical construction warning (docs/ECONOMETRIC_METHODOLOGY.md): provider gross fees are reported as a fee applied to the same provider's reported volume, so a fee-on-volume association largely restates the effective fee rate and must be displayed before any coefficient interpretation.
- Both variables share one provider lineage (Dune); the association is not independent confirmation of anything.
- The output is the named proxy association only; it is never protocol revenue (OQ-01) and H1/H4 stay unresolved (OQ-02 judging risk).
Gate limit codes (all recorded codes visible)
- MECHANICAL_FEE_VOLUME_RELATIONSHIP
- Provider gross fees are constructed from reported volume through an effective fee rate; a fee-on-volume association largely restates that construction and must be displayed before any coefficient interpretation. It is not demand elasticity, not sustainable revenue and not token value.
- PROXY_NOT_PROTOCOL_REVENUE
- The fitted object is the explicitly named proxy only; it must never be labeled protocol revenue, and H1/H4 stay unresolved.
- OQ02_JUDGING_ACCEPTANCE_RISK
- External acceptance of a transparent reproducible proxy association while H1/H4 remain unresolved is an open judging risk (OQ-02).
- SOURCE_REVISIONS_POSSIBLE
- Provider-reported series are revisable; the saved snapshot is not declared final and a revision is a new dataset version, never an overwrite.
- SINGLE_PROVIDER_LINEAGE_NOT_INDEPENDENT
- All model variables come from one provider lineage; their association is not independent confirmation of anything.
- SHORT_SAMPLE_NO_LONG_RUN_CLAIM
- 90 calendar day(s) cannot establish a long-term stable mechanism
- NO_CAUSAL_IDENTIFICATION
- Observational association only: endogeneity, unobserved incentives, common market trends and provider construction remain limitations even with robust standard errors.
Recorded admission reasons
- MECHANICAL_FEE_VOLUME_RELATIONSHIP · limit
- Provider gross fees are constructed from reported volume through an effective fee rate; a fee-on-volume association largely restates that construction and must be displayed before any coefficient interpretation. It is not demand elasticity, not sustainable revenue and not token value.
- PROXY_NOT_PROTOCOL_REVENUE · limit
- The fitted object is the explicitly named proxy only; it must never be labeled protocol revenue, and H1/H4 stay unresolved.
- OQ02_JUDGING_ACCEPTANCE_RISK · limit
- External acceptance of a transparent reproducible proxy association while H1/H4 remain unresolved is an open judging risk (OQ-02).
- SOURCE_REVISIONS_POSSIBLE · limit
- Provider-reported series are revisable; the saved snapshot is not declared final and a revision is a new dataset version, never an overwrite.
- SINGLE_PROVIDER_LINEAGE_NOT_INDEPENDENT · limit
- All model variables come from one provider lineage; their association is not independent confirmation of anything.
- SHORT_SAMPLE_NO_LONG_RUN_CLAIM · limit
- 90 calendar day(s) cannot establish a long-term stable mechanism
- NO_CAUSAL_IDENTIFICATION · limit
- Observational association only: endogeneity, unobserved incentives, common market trends and provider construction remain limitations even with robust standard errors.
Every number above is subject to these limits. Nothing on this page is a causal effect, a demand elasticity or protocol revenue.
Checks and sensitivity
The checks qualify the result. They do not certify a correct specification, and the short sample limits their power.
| Check | Recorded outcome | Implication |
|---|---|---|
| Stationarity · ADF / KPSS | ADF — dependent: null not rejected (p 0.22374995382307866 ≥ 0.05) — unit root treated per the preregistered baseline; explanatory: null not rejected (p 0.12272979670455447 ≥ 0.05) — unit root treated per the preregistered baseline · KPSS — dependent: null not rejected (p 0.1 ≥ 0.05) — stationarity treated per the preregistered baseline; explanatory: null not rejected (p 0.09303176511087341 ≥ 0.05) — stationarity treated per the preregistered baseline | Daily log differences remain the preregistered baseline. |
| Autocorrelation · Ljung–Box | Null not rejected · lags 3, 7 | Weak evidence with this sample; no proof of independence. |
| Heteroskedasticity | Indicated · Breusch–Pagan | The declared HAC covariance is used. Observations are not reweighted. |
| Influence · Cook’s distance | 4 observations flagged (21 Sep, 4 Oct, 5 Oct, 2 Nov) | All flagged observations remain in the sample. |
| HAC lag sensitivity | Fixed lags 3 and 14 | Point estimates are unchanged; uncertainty varies. |
| Alternate source | SRC-DASHBOARD estimated | Shares Dune lineage; no independent confirmation. |
| Alternate price basis | Not applicable | No bound variable uses a swappable price-basis dimension. |
Exact diagnostic p-values
- ADF · dependent
- 0.22374995382307866
- ADF · explanatory
- 0.12272979670455447
- KPSS · dependent
- 0.1
- KPSS · explanatory
- 0.09303176511087341
- Ljung–Box · lag 3
- 0.3682877664693871
- Ljung–Box · lag 7
- 0.8192238468652956
- Breusch–Pagan · LM
- 0.0483069829034806
- Cook’s distance threshold
- flagged observations listed in the recorded battery
- Slope · lags 3
- 0.8542493147420771
- Slope · lags 14
- 0.8542493147420771
- Slope · source_dashboard
- 1.0281410979609282
Full recorded diagnostics (verbatim)
{
"stationarity": {
"note": "ADF (null: unit root) and KPSS (null: stationarity) on the declared LEVEL series over the gate's complete days; settings are preregistered and recorded with every result; undefined tests are null with a reason, never zero",
"tests": [
{
"role": "dependent",
"test": "adf",
"alpha": "0.05",
"status": "computed",
"p_value": 0.22374995382307866,
"settings": {
"maxlag": 7,
"autolag": null,
"rationale": "ADF on the level series with a fixed preregistered lag count (7, matching the documented weekly timing of the baseline) and autolag disabled: the lag count is never selected from the data, and the settings are recorded with the result instead of a pass/fail label (docs/ECONOMETRIC_METHODOLOGY.md 'Specification decision tree' step 2).",
"regression": "c",
"maxlag_rule": "fixed"
},
"used_lag": 7,
"dimension": "SRC-LLAMA",
"metric_id": "provider_gross_fees_usd",
"nobs_used": 82,
"statistic": -2.152983169350026,
"series_key": "provider_gross_fees_usd|SRC-LLAMA",
"interpretation": "the unit-root null is not rejected; the level is treated as nonstationary",
"critical_values": {
"1%": -3.512738056978279,
"5%": -2.8974898650628984,
"10%": -2.585948732897085
},
"null_hypothesis": "unit root",
"decision_at_alpha": "fail_to_reject_null"
},
{
"role": "dependent",
"test": "kpss",
"alpha": "0.05",
"status": "computed",
"p_value": 0.1,
"settings": {
"nlags": 7,
"rationale": "KPSS on the level series with a fixed preregistered lag count (7) as the complement of ADF (null of stationarity); short-sample disagreement between ADF and KPSS is recorded as a warning, never resolved by choosing the convenient answer.",
"nlags_rule": "fixed",
"regression": "c"
},
"dimension": "SRC-LLAMA",
"metric_id": "provider_gross_fees_usd",
"statistic": 0.33713654274426447,
"nlags_used": 7,
"series_key": "provider_gross_fees_usd|SRC-LLAMA",
"boundary_note": "statsmodels reports that the test statistic lies outside the p-value look-up table; the recorded p-value is a table boundary, not an exact tail probability",
"interpretation": "the stationarity null is not rejected; short samples make this weak evidence",
"critical_values": {
"1%": 0.739,
"5%": 0.463,
"10%": 0.347,
"2.5%": 0.574
},
"null_hypothesis": "stationarity",
"decision_at_alpha": "fail_to_reject_null",
"p_value_is_table_boundary": true
},
{
"role": "explanatory",
"test": "adf",
"alpha": "0.05",
"status": "computed",
"p_value": 0.12272979670455447,
"settings": {
"maxlag": 7,
"autolag": null,
"rationale": "ADF on the level series with a fixed preregistered lag count (7, matching the documented weekly timing of the baseline) and autolag disabled: the lag count is never selected from the data, and the settings are recorded with the result instead of a pass/fail label (docs/ECONOMETRIC_METHODOLOGY.md 'Specification decision tree' step 2).",
"regression": "c",
"maxlag_rule": "fixed"
},
"used_lag": 7,
"dimension": "SRC-LLAMA",
"metric_id": "provider_intents_volume_usd",
"nobs_used": 82,
"statistic": -2.4708463638554967,
"series_key": "provider_intents_volume_usd|SRC-LLAMA",
"interpretation": "the unit-root null is not rejected; the level is treated as nonstationary",
"critical_values": {
"1%": -3.512738056978279,
"5%": -2.8974898650628984,
"10%": -2.585948732897085
},
"null_hypothesis": "unit root",
"decision_at_alpha": "fail_to_reject_null"
},
{
"role": "explanatory",
"test": "kpss",
"alpha": "0.05",
"status": "computed",
"p_value": 0.09303176511087341,
"settings": {
"nlags": 7,
"rationale": "KPSS on the level series with a fixed preregistered lag count (7) as the complement of ADF (null of stationarity); short-sample disagreement between ADF and KPSS is recorded as a warning, never resolved by choosing the convenient answer.",
"nlags_rule": "fixed",
"regression": "c"
},
"dimension": "SRC-LLAMA",
"metric_id": "provider_intents_volume_usd",
"statistic": 0.3631663049427737,
"nlags_used": 7,
"series_key": "provider_intents_volume_usd|SRC-LLAMA",
"interpretation": "the stationarity null is not rejected; short samples make this weak evidence",
"critical_values": {
"1%": 0.739,
"5%": 0.463,
"10%": 0.347,
"2.5%": 0.574
},
"null_hypothesis": "stationarity",
"decision_at_alpha": "fail_to_reject_null",
"p_value_is_table_boundary": false
}
]
},
"residual_diagnostics": {
"acf": {
"acf": [
1,
0.01139980494141535,
0.06396609382135686,
-0.06650875632208578,
-0.07532561189600043,
-0.0753648841560947,
0.05567503507415366,
0.010548883901834434
],
"test": "acf",
"status": "computed",
"confint": [
[
1,
1
],
[
-0.1963559619087122,
0.2191555717915429
],
[
-0.14381667029017076,
0.27174885793288445
],
[
-0.27513974484741044,
0.14212223220323889
],
[
-0.2848697367921533,
0.13421851300015245
],
[
-0.28607450271644824,
0.13534473440425887
],
[
-0.15619487146355612,
0.26754494161186343
],
[
-0.20195156189824343,
0.22304932970191227
]
],
"settings": {
"fft": false,
"alpha": "0.05",
"nlags": 7,
"bartlett_confint": true
},
"series_key": "ols_residuals",
"null_hypothesis": "no autocorrelation"
},
"note": "residual battery on the fitted design; the HAC covariance is the preregistered response to any rejection and no observation is removed or repaired",
"influence": {
"test": "cooks_distance",
"policy": "flagged observations are reported and remain in the sample; removal requires an operational reason and a with/without report",
"status": "computed",
"flagged": [
{
"date": "2026-09-21",
"cooks_distance": 0.07569860645830837
},
{
"date": "2026-10-04",
"cooks_distance": 0.04572629113419218
},
{
"date": "2026-10-05",
"cooks_distance": 0.07337335637162182
},
{
"date": "2026-11-02",
"cooks_distance": 1.1490254683093104
}
],
"settings": {
"measure": "cooks_distance",
"rationale": "Cook's distance from the plain OLS fit (leverage/residual geometry, independent of the HAC covariance) with the conventional 4/nobs flagging threshold taken from this config; flagged observations are reported with dates and never removed without an operational reason and a with/without report.",
"threshold_rule": "multiplier_over_nobs",
"threshold_multiplier": "4"
},
"threshold": 0.0449438202247191,
"series_key": "ols_residuals",
"flagged_count": 4,
"covariance_note": "Cook's distance is computed from the plain OLS fit; it is leverage/residual geometry and does not depend on the HAC covariance",
"max_cooks_distance": 1.1490254683093104
},
"ljung_box": {
"test": "ljung_box",
"alpha": "0.05",
"status": "computed",
"results": [
{
"lag": 3,
"lb_stat": 0.8094335781808204,
"lb_p_value": 0.3682877664693871,
"interpretation": "no autocorrelation indicated at this lag (weak evidence)",
"decision_at_alpha": "fail_to_reject_null"
},
{
"lag": 7,
"lb_stat": 2.2111496594309865,
"lb_p_value": 0.8192238468652956,
"interpretation": "no autocorrelation indicated at this lag (weak evidence)",
"decision_at_alpha": "fail_to_reject_null"
}
],
"settings": {
"lags": [
3,
7
],
"model_df_rule": "parameters_count",
"model_df_resolved": 2
},
"series_key": "ols_residuals",
"null_hypothesis": "no autocorrelation up to the declared lag"
},
"residuals": {
"max": 0.8900219743381916,
"min": -2.38030973210424,
"mean": 7.484649604214538e-18,
"nobs": 89,
"note": "residuals are the OLS residuals y - X beta of the fitted design; the HAC covariance changes inference, not residuals",
"series": [
{
"date": "2026-08-07",
"residual": 0.42346959738233697
},
{
"date": "2026-08-08",
"residual": 0.12635293737046915
},
{
"date": "2026-08-09",
"residual": -0.2900779166812659
},
{
"date": "2026-08-10",
"residual": 0.28566314245184454
},
{
"date": "2026-08-11",
"residual": -0.12222555548845221
},
{
"date": "2026-08-12",
"residual": 0.07798963680212187
},
{
"date": "2026-08-13",
"residual": -0.21982309137279
},
{
"date": "2026-08-14",
"residual": 0.1317959676907179
},
{
"date": "2026-08-15",
"residual": -0.18527158117512865
},
{
"date": "2026-08-16",
"residual": 0.10514356558188254
},
{
"date": "2026-08-17",
"residual": 0.1526687619191291
},
{
"date": "2026-08-18",
"residual": -0.02598062564342428
},
{
"date": "2026-08-19",
"residual": 0.41864415564940444
},
{
"date": "2026-08-20",
"residual": 0.37143902145086655
},
{
"date": "2026-08-21",
"residual": 0.030527490676749397
},
{
"date": "2026-08-22",
"residual": 0.05843670503797052
},
{
"date": "2026-08-23",
"residual": -0.06260536488594762
},
{
"date": "2026-08-24",
"residual": -0.2660447825553689
},
{
"date": "2026-08-25",
"residual": 0.05814375946239918
},
{
"date": "2026-08-26",
"residual": -0.07621145834449647
},
{
"date": "2026-08-27",
"residual": 0.2099430274640275
},
{
"date": "2026-08-28",
"residual": 0.076919237772965
},
{
"date": "2026-08-29",
"residual": 0.03970560657089206
},
{
"date": "2026-08-30",
"residual": -0.04730527428881515
},
{
"date": "2026-08-31",
"residual": -0.2887846546402297
},
{
"date": "2026-09-01",
"residual": -0.18573956775908285
},
{
"date": "2026-09-02",
"residual": 0.42712028263613744
},
{
"date": "2026-09-03",
"residual": -0.07534350184407815
},
{
"date": "2026-09-04",
"residual": -0.28563831087641894
},
{
"date": "2026-09-05",
"residual": 0.3807419949673858
},
{
"date": "2026-09-06",
"residual": 0.08914231672590733
},
{
"date": "2026-09-07",
"residual": 0.007990581048746698
},
{
"date": "2026-09-08",
"residual": -0.1293743336801879
},
{
"date": "2026-09-09",
"residual": 0.45865125494583014
},
{
"date": "2026-09-10",
"residual": -0.18443789949552652
},
{
"date": "2026-09-11",
"residual": 0.06047134842322298
},
{
"date": "2026-09-12",
"residual": -0.2997282094104975
},
{
"date": "2026-09-13",
"residual": -0.07459703534754261
},
{
"date": "2026-09-14",
"residual": 0.5327956406782056
},
{
"date": "2026-09-15",
"residual": 0.0355640664317803
},
{
"date": "2026-09-16",
"residual": 0.20834348095989424
},
{
"date": "2026-09-17",
"residual": 0.16564946961190907
},
{
"date": "2026-09-18",
"residual": -0.022307782348357286
},
{
"date": "2026-09-19",
"residual": 0.021873190386368258
},
{
"date": "2026-09-20",
"residual": -0.24139223038986662
},
{
"date": "2026-09-21",
"residual": 0.48463629455197554
},
{
"date": "2026-09-22",
"residual": -0.14701810081714065
},
{
"date": "2026-09-23",
"residual": -0.08314330585658761
},
{
"date": "2026-09-24",
"residual": -0.06494183507380519
},
{
"date": "2026-09-25",
"residual": -0.019803018259087347
},
{
"date": "2026-09-26",
"residual": 0.016625571104469827
},
{
"date": "2026-09-27",
"residual": 0.17817253030814195
},
{
"date": "2026-09-28",
"residual": 0.03884334649524568
},
{
"date": "2026-09-29",
"residual": 0.022309940011032092
},
{
"date": "2026-09-30",
"residual": -0.017098063929269908
},
{
"date": "2026-10-01",
"residual": -0.012465359185114655
},
{
"date": "2026-10-02",
"residual": 0.12184476295338606
},
{
"date": "2026-10-03",
"residual": -0.20855870961283896
},
{
"date": "2026-10-04",
"residual": 0.8900219743381916
},
{
"date": "2026-10-05",
"residual": -0.6343808717693038
},
{
"date": "2026-10-06",
"residual": 0.04785948524715314
},
{
"date": "2026-10-07",
"residual": 0.13209800533627072
},
{
"date": "2026-10-08",
"residual": -0.10302815402255217
},
{
"date": "2026-10-09",
"residual": -0.13807007749433536
},
{
"date": "2026-10-10",
"residual": 0.02970856040305192
},
{
"date": "2026-10-11",
"residual": -0.08533552962792704
},
{
"date": "2026-10-12",
"residual": 0.13959951447195382
},
{
"date": "2026-10-13",
"residual": 0.05491356393787497
},
{
"date": "2026-10-14",
"residual": 0.09095118144876849
},
{
"date": "2026-10-15",
"residual": 0.053858336338161394
},
{
"date": "2026-10-16",
"residual": -0.04233196612990964
},
{
"date": "2026-10-17",
"residual": 0.09036535960276076
},
{
"date": "2026-10-18",
"residual": 0.14113508479101564
},
{
"date": "2026-10-19",
"residual": -0.1869498619505527
},
{
"date": "2026-10-20",
"residual": 0.2143491454304824
},
{
"date": "2026-10-21",
"residual": -0.18164469228345367
},
{
"date": "2026-10-22",
"residual": -0.14525871910037638
},
{
"date": "2026-10-23",
"residual": 0.22426442891653248
},
{
"date": "2026-10-24",
"residual": 0.22573850968279474
},
{
"date": "2026-10-25",
"residual": -0.2646935052430921
},
{
"date": "2026-10-26",
"residual": 0.08302377473287131
},
{
"date": "2026-10-27",
"residual": -0.201248006524613
},
{
"date": "2026-10-28",
"residual": 0.13342306721228162
},
{
"date": "2026-10-29",
"residual": 0.1502893544545943
},
{
"date": "2026-10-30",
"residual": 0.4347868894696629
},
{
"date": "2026-10-31",
"residual": -0.28126739554146085
},
{
"date": "2026-11-01",
"residual": 0.10172033777978434
},
{
"date": "2026-11-02",
"residual": -2.38030973210424
},
{
"date": "2026-11-03",
"residual": -0.7752891783644837
}
],
"std_ddof1": 0.3520142956983182
},
"heteroskedasticity": {
"test": "breusch_pagan",
"alpha": "0.05",
"f_stat": 3.986297649279017,
"status": "computed",
"lm_stat": 3.899273846193741,
"settings": {
"test": "breusch_pagan",
"robust": true,
"exog_het": "design_exog",
"rationale": "Breusch-Pagan is the methodology's named example ('Estimation and diagnostics'); the fitted design columns are the heteroskedasticity regressors, no extra series is introduced, and the preregistered response to a rejection is the already-declared HAC covariance - no reweighting, no outlier removal."
},
"f_p_value": 0.048998117161143426,
"lm_p_value": 0.0483069829034806,
"series_key": "ols_residuals",
"interpretation": "heteroskedasticity indicated; the preregistered HAC covariance is the declared response, nothing is reweighted or removed",
"null_hypothesis": "homoskedasticity",
"decision_at_alpha": "reject_null"
}
},
"sensitivity": {
"note": "only the preregistered config presets run here; a variant that binds nothing or is not admissible is reported with a reason instead of being invented; variants are robustness checks, never second results",
"presets": [
{
"kind": "covariance_maxlags",
"nobs": 89,
"status": "estimated",
"variant": "same design, alternative fixed HAC lag count; point estimates do not move with the covariance setting",
"preset_id": "hac_maxlags_3",
"rationale": "Preregistered HAC lag sensitivity (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7, sensitivity 3 and 14 when the sample supports it); a fixed alternative lag count, never selected from the data.",
"coefficients": [
{
"name": "const",
"t_stat": -0.7327516801620056,
"p_value": 0.46567956312782843,
"ci95_low": -0.10282767070113566,
"estimate": -0.02769749207193234,
"ci95_high": 0.04743268655727098,
"std_error": 0.03779928838349254
},
{
"name": "dlog(provider_intents_volume_usd|SRC-LLAMA)",
"t_stat": 5.889425186343956,
"p_value": 7.13196828944432e-8,
"ci95_low": 0.5659507187330541,
"estimate": 0.8542493147420771,
"ci95_high": 1.1425479107511,
"std_error": 0.14504799495931434
}
],
"variant_settings": {
"covariance": {
"type": "HAC",
"use_t": true,
"kernel": "bartlett",
"maxlags": 3,
"rationale": "Newey-West HAC on daily observations with the documented baseline weekly timing (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7); the lag count is a fixed preregistered number, never selected from the sample or from p-values; sensitivity lags (3 and 14) belong to the NI-053 robustness artifacts, not to this baseline fit.",
"maxlags_rule": "fixed",
"use_correction": true
}
}
},
{
"kind": "covariance_maxlags",
"nobs": 89,
"status": "estimated",
"variant": "same design, alternative fixed HAC lag count; point estimates do not move with the covariance setting",
"preset_id": "hac_maxlags_14",
"rationale": "Preregistered HAC lag sensitivity (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7, sensitivity 3 and 14 when the sample supports it); a fixed alternative lag count, never selected from the data.",
"coefficients": [
{
"name": "const",
"t_stat": -0.7826909788401359,
"p_value": 0.43593387931534017,
"ci95_low": -0.09803401837635091,
"estimate": -0.02769749207193234,
"ci95_high": 0.04263903423248623,
"std_error": 0.03538751923904508
},
{
"name": "dlog(provider_intents_volume_usd|SRC-LLAMA)",
"t_stat": 5.936003938569658,
"p_value": 5.829303379311061e-8,
"ci95_low": 0.5682129458153626,
"estimate": 0.8542493147420771,
"ci95_high": 1.1402856836687916,
"std_error": 0.14390982950525424
}
],
"variant_settings": {
"covariance": {
"type": "HAC",
"use_t": true,
"kernel": "bartlett",
"maxlags": 14,
"rationale": "Newey-West HAC on daily observations with the documented baseline weekly timing (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7); the lag count is a fixed preregistered number, never selected from the sample or from p-values; sensitivity lags (3 and 14) belong to the NI-053 robustness artifacts, not to this baseline fit.",
"maxlags_rule": "fixed",
"use_correction": true
}
}
},
{
"kind": "source_alternative",
"nobs": 89,
"sample": {
"complete_days": 90,
"usable_observations": 89
},
"status": "estimated",
"preset_id": "source_dashboard",
"rationale": "Preregistered source sensitivity: re-estimation on the parallel dashboard-reported provider series (same metric definitions, alternate source dimension). The variant is a robustness check, never a second result; both sources share Dune lineage, so agreement is not independent confirmation.",
"coefficients": [
{
"name": "const",
"t_stat": -0.7673078194213402,
"p_value": 0.4449759171632114,
"ci95_low": -0.10105149566096921,
"estimate": -0.028145177545132904,
"ci95_high": 0.044761140570703406,
"std_error": 0.03668042581184484
},
{
"name": "dlog(provider_intents_volume_usd|SRC-DASHBOARD)",
"t_stat": 11.153260715023508,
"p_value": 1.862841511435972e-18,
"ci95_low": 0.8449173920722496,
"estimate": 1.0281410979609282,
"ci95_high": 1.211364803849607,
"std_error": 0.0921830058698454
}
],
"variant_settings": {
"swaps": [
{
"role": "dependent",
"metric_id": "provider_gross_fees_usd",
"to_dimension": "SRC-DASHBOARD",
"from_dimension": "SRC-LLAMA"
},
{
"role": "explanatory[0]",
"metric_id": "provider_intents_volume_usd",
"to_dimension": "SRC-DASHBOARD",
"from_dimension": "SRC-LLAMA"
}
],
"variant_spec_id": "gross_fee_activity_v1+source_dashboard"
},
"variant_admission": {
"note": "the variant is a preregistered robustness re-estimation, never a second published result; the gate evaluation is recorded so a rejected variant is never estimated",
"verdict": "READY_WITH_LIMITS",
"rejecting_codes": []
}
},
{
"kind": "price_basis_alternative",
"status": "not_applicable",
"preset_id": "price_basis_end",
"rationale": "Preregistered price-mark sensitivity: re-estimation with every bound price-basis series moved to the end-of-day mark (docs/ECONOMETRIC_METHODOLOGY.md 'Preparation and transform policy': alternate daily price marks). It applies only where a bound variable is valued through a declared daily price basis; a model whose variables are provider-reported USD as-is records the preset as not applicable instead of inventing a variant.",
"reason_code": "no_bound_variable_to_swap",
"reason_detail": "no bound variable uses a swappable dimension of this kind; the variant is not applicable to this specification and is not invented (preset dimension kind: price_basis_alternative)"
}
]
},
"diagnostics_config": {
"spec_id": "gross_fee_activity_v1",
"residuals": {
"acf": {
"fft": false,
"alpha": "0.05",
"nlags": 7,
"bartlett_confint": true
},
"influence": {
"measure": "cooks_distance",
"rationale": "Cook's distance from the plain OLS fit (leverage/residual geometry, independent of the HAC covariance) with the conventional 4/nobs flagging threshold taken from this config; flagged observations are reported with dates and never removed without an operational reason and a with/without report.",
"threshold_rule": "multiplier_over_nobs",
"threshold_multiplier": "4"
},
"ljung_box": {
"lags": [
3,
7
],
"rationale": "Small lags 3 and the baseline weekly timing 7 (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': Ljung-Box at justified small lags); model_df consumes the design's estimated parameter count (intercept plus slopes), so the smallest declared lag must exceed it - lags 1/2 would have non-positive adjusted dof and are not declared instead of being reported as undefined.",
"model_df_rule": "parameters_count"
},
"heteroskedasticity": {
"test": "breusch_pagan",
"robust": true,
"exog_het": "design_exog",
"rationale": "Breusch-Pagan is the methodology's named example ('Estimation and diagnostics'); the fitted design columns are the heteroskedasticity regressors, no extra series is introduced, and the preregistered response to a rejection is the already-declared HAC covariance - no reweighting, no outlier removal."
}
},
"fit_config": "gross_fee_activity_v1_fit.json",
"config_hash": "7c7c256109a74b3c6fa748b9d6369c450841bcfe01b817c3bcbff652cadbcce5",
"spec_config": "gross_fee_activity_v1.json",
"stationarity": {
"adf": {
"maxlag": 7,
"autolag": null,
"rationale": "ADF on the level series with a fixed preregistered lag count (7, matching the documented weekly timing of the baseline) and autolag disabled: the lag count is never selected from the data, and the settings are recorded with the result instead of a pass/fail label (docs/ECONOMETRIC_METHODOLOGY.md 'Specification decision tree' step 2).",
"regression": "c",
"maxlag_rule": "fixed"
},
"kpss": {
"nlags": 7,
"rationale": "KPSS on the level series with a fixed preregistered lag count (7) as the complement of ADF (null of stationarity); short-sample disagreement between ADF and KPSS is recorded as a warning, never resolved by choosing the convenient answer.",
"nlags_rule": "fixed",
"regression": "c"
},
"series": [
"dependent",
"explanatory"
],
"min_observations": 20
},
"decision_alpha": "0.05",
"gate_policy_config": "readiness_gate_policy_v1.json",
"sensitivity_presets": [
{
"kind": "covariance_maxlags",
"maxlags": 3,
"preset_id": "hac_maxlags_3",
"rationale": "Preregistered HAC lag sensitivity (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7, sensitivity 3 and 14 when the sample supports it); a fixed alternative lag count, never selected from the data."
},
{
"kind": "covariance_maxlags",
"maxlags": 14,
"preset_id": "hac_maxlags_14",
"rationale": "Preregistered HAC lag sensitivity (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7, sensitivity 3 and 14 when the sample supports it); a fixed alternative lag count, never selected from the data."
},
{
"kind": "source_alternative",
"source": "SRC-DASHBOARD",
"preset_id": "source_dashboard",
"rationale": "Preregistered source sensitivity: re-estimation on the parallel dashboard-reported provider series (same metric definitions, alternate source dimension). The variant is a robustness check, never a second result; both sources share Dune lineage, so agreement is not independent confirmation.",
"applies_to_metric_ids": [
"provider_gross_fees_usd",
"provider_intents_volume_usd"
]
},
{
"kind": "price_basis_alternative",
"preset_id": "price_basis_end",
"rationale": "Preregistered price-mark sensitivity: re-estimation with every bound price-basis series moved to the end-of-day mark (docs/ECONOMETRIC_METHODOLOGY.md 'Preparation and transform policy': alternate daily price marks). It applies only where a bound variable is valued through a declared daily price basis; a model whose variables are provider-reported USD as-is records the preset as not applicable instead of inventing a variant.",
"price_basis": "end",
"applies_to_metric_ids": [
"near_price_usd"
]
}
],
"diagnostics_config_id": "gross_fee_activity_v1_diagnostics",
"interpretation_template": "bounded_association_v1",
"diagnostics_config_version": 1
}
}What was estimated
An observational model of daily changes in provider-reported gross fees and Intents volume. The specification and covariance settings were fixed before estimation.
- Dependent variable
- Provider gross fees · USD · daily log difference
- Explanatory variable
- Intents volume · USD · daily log difference
- Metric IDs
- provider_gross_fees_usd | SRC-LLAMA · provider_intents_volume_usd | SRC-LLAMA
- Specification
- gross_fee_activity_v1 · version 1 · proxy family
- Estimator
- OLS with intercept; one substantive slope
- Covariance
- HAC · bartlett · 7 fixed lags · t inference and correction enabled
- Input window · UTC
- [6 Aug 2026, 4 Nov 2026)
- Fitted observations
- 89 · 6 Aug – 3 Nov 2026 · calendar-adjacent differences
- R² / adjusted R²
- 0.4343881092500226 / 0.42788682314944804
- Design rank
- 2 · condition number 2.784927388082539
Raw specification (recorded verbatim)
{
"family": "proxy",
"spec_id": "gross_fee_activity_v1",
"baseline": "difference_log",
"frequency": "daily",
"intercept": true,
"config_hash": "f5ae1bc621f2499decfaef642251d72746fb25d8e093a4fa88a35953ab697b06",
"spec_version": 1,
"declared_target": "gross_provider_fees",
"dependent_variable": {
"dimension": "SRC-LLAMA",
"metric_id": "provider_gross_fees_usd"
},
"substantive_slopes": 1,
"explanatory_variables": [
{
"role": "activity",
"dimension": "SRC-LLAMA",
"metric_id": "provider_intents_volume_usd"
}
]
}{
"rank": 2,
"rows": 89,
"columns": [
"const",
"dlog(provider_intents_volume_usd|SRC-LLAMA)"
],
"baseline": "difference_log",
"source_hash": "e3b8c5d7585a3a0b9a7856fff1bb78e90550922a49c69453b2cfed5bb6fbf5fd",
"transformations": {
"baseline": "difference_log",
"float64_conversion": "explicit_finite_required",
"difference_adjacency": "calendar_adjacent_only",
"strictly_positive_required": true
},
"condition_number": 2.784927388082539,
"source_hash_note": "SHA-256 over the canonical JSON of the exact Decimal source values and calendar dates behind every design row; stable across platforms.",
"float64_hash_note": "SHA-256 over the IEEE-754 hex serialization of the float64 design; byte-identical on the same platform. Across platforms libm rounding may differ at the last unit in the last place; cross-platform reproduction is governed by the declared tolerance comparison, not by this digest.",
"float64_hex_sha256": "8532e0ffb9aebb603ca88d2b713a444d1c831a70a1f407a96f416e8ab202db14"
}{
"nobs": 89,
"method": "OLS",
"df_resid": 87,
"estimator": "ols",
"r_squared": 0.4343881092500226,
"covariance": {
"type": "HAC",
"use_t": true,
"kernel": "bartlett",
"maxlags": 7,
"rationale": "Newey-West HAC on daily observations with the documented baseline weekly timing (docs/ECONOMETRIC_METHODOLOGY.md 'Estimation and diagnostics': baseline maxlags 7); the lag count is a fixed preregistered number, never selected from the sample or from p-values; sensitivity lags (3 and 14) belong to the NI-053 robustness artifacts, not to this baseline fit.",
"maxlags_rule": "fixed",
"use_correction": true
},
"adj_r_squared": 0.42788682314944804,
"p_value_usage": "p-values are recorded for the declared output contract only; no variable, lag, sample or specification choice was made from them (no p-value-driven search anywhere in this estimator)."
}A fixed, reproducible record
This result is attached to the published snapshot. Its inputs, configuration and environment are pinned in the manifest.
- Run ID
- 66ef8565-b699-565c-a85b-bde91228661b
- Published
- 14 Nov 2026, 13:04:57 UTC
- Approval
- ILYUTKICK (owner) via interactive ZCode role-A coordinator session, 2026-11-14 · approve
- Review reference
- NI-079 final local release acceptance (PR #102): owner instruction to complete all acceptance stages; fit admission READY_WITH_LIMITS with the mechanical-construction limitation displayed; readiness PROXY_ONLY_READY_WITH_LIMITS
- Gate verdict
- Published with limits
- Overall readiness
- PROXY_ONLY_READY_WITH_LIMITS
- Code commit
- 2a54760eb9521b76a560227af88cde33af37c9ac
- Tolerances
- rtol 0.00000001 · atol 0.0000000001
- Environment
- CPython 3.13.3 · NumPy 2.5.4 · SciPy 1.18.1 · statsmodels 0.15.0 · macOS arm64
The reproduction bundle
The full manifest bundle downloads below. It is served immutably with a run-id ETag; the same bytes are what the publication gate verified.
A reviewer re-verifies the downloaded bundle offline — no operator tooling, no network, no re-fit — with:
python -m near_api.research verify --bundle <downloaded.json> --offlineRecorded environment and reproduction contract (verbatim)
{
"blas": {
"blas": "accelerate",
"lapack": "accelerate"
},
"platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
"randomness": "none; the estimator is fully deterministic and needs no seed",
"numpy_version": "2.5.4",
"scipy_version": "1.18.1",
"python_version": "3.13.3 (v3.13.3:6280bb54784, Apr 8 2025, 10:47:54) [Clang 15.0.0 (clang-1500.3.9.4)]",
"statsmodels_version": "0.15.0",
"python_implementation": "CPython"
}{
"command": "uv run --project apps/api python -m near_api.research fit --archive-root <research root> --out-dir <output directory> --code-commit <git sha>",
"determinism": "no wall-clock fields; inputs pinned by SHA-256; exact decimal source values hashed for the design; fitted floats compared under the declared tolerances",
"coefficient_atol": "0.0000000001",
"coefficient_rtol": "0.00000001"
}