Forecasting daily political opinion polls using the fractionally cointegrated VAR model
Bibliographic record
Abstract
We examine forecasting performance of the recent fractionally cointegrated vector autoregressive (FCVAR) model. The model is applied to daily polling data of political support in the United Kingdom for 2010 - 2015. We compare with popular competing models and at various forecast horizons. Our findings show that the precision of fore- casts generated by the FCVAR model is better than all multivariate and univariate models in the portfolio, and the four variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy. Furthermore, the FCVAR model significantly outperforms the standard cointegrated VAR (CVAR) model at all forecast horizons and the relative forecast improvement is highest at longer forecast horizons, where the root mean squared forecast error of the FCVAR model is up to 20% lower than that of the CVAR benchmark model. In an empirical application to the prediction of vote shares in the 2015 UK general election, forecasts generated by the FCVAR model leading into the election appear to provide a more informative assessment of the current state of public opinion on electoral support than that suggested by the hung government prediction of the opinion poll. Specifically, the FCVAR model projects the correct direction for the realized vote shares in the election for both the Conservative and Labour parties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".