Tools For Modeling Sparse Vector Autoregressions
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Bibliographic record
Abstract
The Proposed VARX-L Penalty Functions.Note that ( ) on and ( ) off denote the diagonal and off-diagonal elements of coefficient matrix ( ) , respectively. . . .1.2 One-step and four-step ahead MSFE of k = 20 macroeconomic indicators (relative to sample mean) with m = 20 exogenous predictors p = 4, s = 4. . . . . . .1.3 One-step ahead and four-step ahead MSFE (relative to sample mean) for VARX forecasts of k = 4 Canadian macroeconomic indicators with m = 20 exogenous predictors p = 4, s = 4 and VAR forecasts of 4 Canadian macroeconomic indicators, p = 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.4 One-step and four-step ahead MSFE (relative to a random walk) for k = 20 nonstationary macroeconomic indicators with m=20 exogenous predictors which shrink toward a vector random walk. . . . . . . . . . . . . . . . . . . . . .1.5 Out of sample MSFE of one-step ahead forecasts after 100 simulations: Scenario 1.Standard errors are shown in parentheses. . . . . . . . . . . . . . . . . .1.6 Out of sample MSFE of one-step ahead forecasts after 100 simulations: Scenario 2. Standard errors are shown in parentheses. . . . . . . . . . . . . . . . . .1.7 Out of sample MSFE of one-step ahead forecasts after 100 simulations: Scenario 3. Standard errors are shown in parentheses. . . . . . . . . . . . . . . . . .1.8 Out of sample MSFE of one-step ahead forecasts after 100 simulations: Scenario 4. Standard errors are shown in parentheses. . . . . . . . . . . . . . . . . .2.1 Out-of-sample mean-squared one-step-ahead forecast error (standard errors are in parentheses) for Scenario 1 based on 100 simulations. . . . . . . . . . . . .2.2 Out-of-sample mean-squared one-step-ahead forecast error (standard errors are in parentheses) for Scenario 2 based on 100 simulations. . . . . . . . . . . . .2.3 Out-of-sample mean-squared one-step-ahead forecast error (standard errors are in parentheses) for Scenario 3 based on 100 simulations. . . . . . . . . . . . .2.4 Lag selection performance (standard errors in parentheses) for Scenario 1 based on 100 simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.5 Lag selection performance (standard errors in parentheses) for Scenario 2 based on 100 simulations. . . . . . . . . . . . . .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it