Modeling the relationship between climate oscillations and drought by a multivariate GARCH model
Bibliographic record
Abstract
Typical multivariate time series models may exhibit comovement in mean but not in variance of hydrologic and climatic variables. This paper introduces multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models to capture the comovement of the variance or the conditional covariance between two hydroclimatic time series. The diagonal vectorized and Baba-Engle-Kroft-Kroner models are developed to evaluate the covariance between drought and two atmospheric circulations, Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) time series during 1954-2000. The univariate generalized autoregressive conditional heteroscedasticity model indicates a strong persistency level in conditional variance for NAO and a moderate persistency level for SOI. The conditional variance of short-term drought index indicates low level of persistency, while the long-term index drought indicates high level of persistency in conditional variance. The estimated conditional covariance between drought and atmospheric indices is shown to be weak and negative. It is also observed that the covariance between drought and atmospheric indices is largely dependent on short-run variance of atmospheric indices rather than their long-run variance. The nonlinearity and stationarity tests show that the conditional covariances are nonlinear but stationary. However, the degree of nonlinearity is higher for the covariance between long-term drought and atmospheric indices. It is also observed that the nonlinearity of NAO is higher than that for SOI, in contrast to the stationarity which is stronger for SOI time series. Key Points Multivariate heteroscedastic models are developed for drought analysis Conditional covariance between drought, SOI, and NAO is not strong Time-varying correlations between drought and atmospheric indices are estimated.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".