THE FAST DECAY PROCESS IN RECREATIONAL DEMAND ACTIVITIES AND THE USE OF ALTERNATIVE COUNT DATA MODELS
Bibliographic record
Abstract
Since the early 1990s, researchers have routinely used count data models (such as the Poisson and negative binomial) to estimate the demand for recreational activities. Along with the success and popularity of count data models in recreational demand analysis during the last decade, a number of shortcomings of standard count data models became obvious to researchers. This had led to the development of new and more sophisticated model specifications. Furthermore, semi-parametric and non-parametric approaches have also made their way into count data models. Despite these advances, however, one interesting issue has received little research attention in this area. This is related to the fast decay process of the dependent variable and the associated long tail. This phenomenon is observed quite frequently in recreational demand studies; most recreationists make one or two trips while a few of them make exceedingly large number of trips. This introduces an extreme form of overdispersion difficult to address in popular count data models. The major objective of this paper is to investigate the issues related to proper modelling of the fast decay process and the associated long tails in recreation demand analysis. For this purpose, we introduce two categories of alternative count data models. The first group includes four alternative count data models, each characterised by a single parameter while the second group includes one count data model characterised by two parameters. This paper demonstrates how these alternative models can be used to properly model the fast decay process and the associated long tail commonly observed in recreation demand analysis. The first four alternative count data models are based on an adaptation of the geometric, Borel, logarithmic and Yule probability distributions to count data models while the second group of models relied on the use of the generalised Poisson probability distribution. All these alternative count data models are empirically implemented using the maximum likelihood estimation procedure and applied to study the demand for moose hunting in Northern Ontario. Econometric results indicate that most of the alternative count data models proposed in this paper are able to capture the fast decay process characterising the number of moose hunting trips. Overall they seem to perform as well as the conventional negative binomial model and better than the Poisson specification. However further investigation of the econometric results reveal that the geometric and generalised Poisson model specifications fare better than the modified Borel and Yule regression models.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".