A Semi-Parametric Integer-Valued Autoregressive Model with Covariates
Bibliographic record
Abstract
Abstract We consider a low count data INAR (Integer Autoregressive Regression) model in which the arrivals are modelled non-parametrically and are allowed to contain covariates. Accommodating possible covariates is important as exogenous variability, such as seasonality, often needs to be catered for. The main challenge is to maintain the axiomatic properties of the arrivals non-parametric mass function while, at the same time, incorporating covariates directly into the associated probabilities. Compared with models that impose standard distributions such as Poisson or Negative Binomial for the arrivals, our approach is more flexible and provides a general arrival specification. The dependence structure is parametric and uses the standard binomial thinning operator. The parameters are estimated by the Maximum Likelihood. Monte Carlo simulations show that our proposed model performs very well with good finite sample results. Two empirical issues are addressed where incorporating covariates is a prerequisite for successful modelling. The first incorporates seasonal covariates into a semi-parametric model for forecasting the numbers of claimants of wage loss benefits in the logging industry in British Columbia, Canada. The second investigates if macro-economic indicators in an economy may be useful in predicting the number of bank failures in the US financial sector.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".