The Fast Decay Process in Recreational Demand Activities and the use of Alternative Count Data Models

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.fast decay process, recreational demand, count data models, Borel, Yule, logarithmic and generalised Poisson regression models, Resource /Energy Economics and Policy,

THE FAST DECAY PROCESS IN RECREATIONAL DEMAND ACTIVITIES AND THE USE OF ALTERNATIVE COUNT DATA MODELS

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.Fast Decay Process, Recreational Demand, Count Data Models, Borel, Yule, logarithmic and generalised Poisson regression models, Resource /Energy Economics and Policy,

Rat floods and water floods: the ecological and sociological dynamics of rodent management in Bangladesh

Chakma, N., Belmain, S.R., Sarker, N.J., Sarker, S.U., Kamal, N.Q., Sarker, S.K

A Novel Pathogenic Avipoxvirus Infecting Vulnerable Cook’s Petrel (Pterodroma cookii) in Australia Demonstrates a High Genomic and Evolutionary Proximity with South African Avipoxviruses

Avipoxviruses are assumed to be restricted to avian hosts and are considered to be important viral pathogens that may impact the conservation of many vulnerable or endangered birds. Recent reports of avipoxvirus-like viruses from reptiles suggest that cross-species transmission may be possible within birds and other species. Most of the avipoxviruses in wild and sea birds remain uncharacterized, and their genetic variability is unclear. Here, cutaneous pox lesions were used to recover a novel, full-length Cook’s petrelpox virus (CPPV) genome from a vulnerable Cook’s petrel (Pterodroma cookii), and this was followed by the detection of immature virions using transmission electron microscopy (TEM). The CPPV genome was 314,065 bp in length and contained 357 predicted open-reading frames (ORFs). While 323 of the ORFs of the CPPV genome had the greatest similarity with the gene products of other avipoxviruses, a further 34 ORFs were novel. Subsequent phylogenetic analyses showed that the CPPV was most closely related to other avipoxviruses that were isolated mostly from South African bird species and demonstrated the highest sequence similarity with a recently isolated flamingopox virus (88.9%) in South Africa. Considering the sequence similarity observed between CPPV and other avipoxviruses, TEM evidence of poxvirus particles, and phylogenetic position, this study concluded that CPPV is a distinct candidate of avipoxviruses

A quantitative model for disruption mitigation in a supply chain

© 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches