A multi-inflated hurdle regression model for the total number of overnight stays of Italian tourists in the years of the economic recession
Bibliographic record
Abstract
This contribution is concerned with the tourism behavior of Italian residents in the period covering the last economic recession: it investigates whether and how the economic recession has affected the total number of overnight stays in a quarter by modelling it through a hurdle multi-inflated regression model. The assumptions of the hurdle model are consistent with the phenomenon under study, in which firstly a person decides whether to have a vacation trip and then, conditionally to a positive decision, he decides the number of overnight stays. Therefore the binary process concerning the decision to have at least a vacation in a given quarter is modelled through a logit regression model. Then the total number of overnight stays, for those who had at least a vacation, is modelled. Since this variable is naturally concentrated on some specific values (like 2, 6, 7, 14, 20 nights), we use a Multi-Inflated Truncated Negative Binomial regression model in order to control for this peculiar peaks. We analyse data from the quarterly survey on Trips and Holidays in Italy and Abroad carried out by the Italian National Institute of Statistics, in the period 2004 to 2013. The empirical results show that socio-economic characteristics of the individuals and of their families have an important effect on their tourism participation; that these factors, together with some trips-related characteristics, affect the total number of overnight stays; and that the economic recession impacted negatively on both aspects of tourism behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".