nep-cul New Economics Papers
on Cultural Economics
Issue of 2007‒07‒20
five papers chosen by
Roberto Zanola
University of the Piemonte Orientale

  1. An Application of Extreme Value Theory to U.S. Movie Box Office Returns By Guang Bi; David E. Giles
  2. Heritage Planning: Approaches to Public Participation and Contestation in Groningen, The Netherlands By Stephanie Ashe
  3. Demand-Driven Scheduling of Movies in a Multiplex By Eliashberg, J.; Hegie, Q.; Ho, J.; Huisman, D.; Miller, S.J.; Swami, S.; Weinberg, C.B.; Wierenga, B.
  4. Returns in Cost Diseased Markets with Psychic Benefits: Two Apparently Conflicting Models of Equilibrium By William J. Baumol
  5. The Link between Perceived and Actual Wildfire Danger: An Economic and Spatial Analysis Study in Colorado (USA) By Pamela Kaval

  1. By: Guang Bi (Department of Economics, University of Victoria); David E. Giles (Department of Economics, University of Victoria)
    Abstract: In this paper we use extreme value theory to model the U.S. movie box-office returns, using weekly data for the period January 1982 to September 2006. The Peaks over Threshold method is used to fit the Generalized Pareto Distribution to the tails of the distributions of both positive weekly returns, and negative returns. Tail risk measures such as value-at-risk and expected shortfall are computed using likelihood and profile likelihood methods. These measures can be used as indicators for the film distributors in the preparation of movie prints, or as references for actual or potential investors in the movie industry.
    Keywords: Movie revenue, extreme values, generalized Pareto distribution, value at risk
    JEL: C16 C46 G1 Z1
  2. By: Stephanie Ashe
    Date: 2007–06–22
  3. By: Eliashberg, J.; Hegie, Q.; Ho, J.; Huisman, D.; Miller, S.J.; Swami, S.; Weinberg, C.B.; Wierenga, B. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: This paper describes a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies within each day of the week, on which screen(s) different movies will be played, and at which time(s). The model consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time; and (ii) an optimization procedure that quickly finds an almost optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule we formulate the so-called movie scheduling problem as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We have applied this combined demand forecasting /schedule optimization procedure to a multiplex in Amsterdam where we supported the scheduling of fourteen movie weeks. The proposed model not 2 only makes movie scheduling easier and less time consuming, but also generates schedules that would attract more visitors than the current ?intuition-based? schedules.
    Keywords: Optimization of movie schedules;Integer programming;Column generation;Demand forecasting;
    Date: 2007–05–10
  4. By: William J. Baumol
    Keywords: Baumol's Disease, Return on Investment in Art
    JEL: G11 Z11
    Date: 2007–06
  5. By: Pamela Kaval (University of Waikato)
    Abstract: Over the last 20 years, costs for wildfire initial attack in the U.S. have increased significantly. The increased cost relates to wildfire suppression practices as well as the growing number of wildland urban interface (WUI) homes. Requiring WUI residents to pay an annual tax for their wildfire risk would lower costs to the general taxpayer. Willingness-to-pay (WTP) for wildfire prevention, in relation to both perceived and actual wildfire danger, was the focus of this study. Colorado WUI residents had a high awareness of wildfire risk and were willing to pay over $400 annually to reduce this risk. Respondents beliefs about wildfire frequency were comparable to the original natural wildfire regimes of their areas pre-European settlement.
    Keywords: GIS; wildfire risk; stakeholder; contingent valuation; Colorado
    JEL: Q26
    Date: 2007–07–13

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