Local Whittle Analysis of Stationary Fractional Cointegration
Bibliographic record
Abstract
We consider a local Whittle analysis of a stationary fractionally cointegrated model. A two step estimator equivalent to the local Whittle QMLE is proposed to jointly estimate the integration orders of the regressors, the integration order of the errors, and the cointegration vector. The estimator is semiparametric in the sense that it employs local assumptions on the joint spectral density matrix of the regressors and the errors near the zero frequency. We show that, for the entire stationary region of the integration orders, the estimator is asymptotically normal with block diagonal covariance matrix. Thus, the estimates of the integration orders are asymptotically independent of the estimate of the cointegration vector. Furthermore, our estimator of the cointegrating vector is asymptotically normal for a wider range of integration orders than the narrow band frequency domain least squares estimator and is superior with respect to asymptotic variance. An application to financial volatility series is offered.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".