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By ALEJANDRO JOSE GENTILE
During this publication the writer LISTS a few developments IN TOURISM AND PROPOSES a few ideas to enhance COMPETITIVENESS OF TOURISM locations AND raise THE variety of viewers THAT THEY obtain.
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5 3 In (review time) Fig. 2. Nonparametric estimates of the approval hazard, 1985–95 (first eighteen months of NDA review) Conclusion While instructive, the theoretical and empirical analyses presented here could doubtless be improved and extended in fascinating ways. Theoretically, it would be interesting to incorporate a budget into the model and allow the agency to bestow resources explicitly upon drugs. A more interesting issue, to my mind, concerns the organizational factors that affect information processing.
Second, the model shows that waiting can be a de facto form of rejection. Even where no formal rejection option exists, long-term waiting increasingly implies a zero probability of eventual approval under the optimum policy. Finally, the model offers some important lessons for hazard estimation. The likelihood employed should be inverse Gaussian, and, more significantly, the hazard function should be nonmonotonic. This rules out the simple exponential distribution as a hazard specification, but it also rules out the most commonly used parametric distribution of political science duration analysis: the Weibull distribution.
Notice that a greater approval payoff increases the approval probability. The following propositions note interesting properties of the approval distribution. PROPOSITION 2: DE FACTO REJECTION WITHOUT A REJECTION OPTION. In expectation, a nonzero fraction of drugs will never receive the agency’s approval, as lim G*(t) Ͻ 1. tlϱ SKETCH OF PROOF. The proof comes from examination of G*(t) in (7). Because lim r(t) ϭ s Ͻ ϱ (eq. 3), the term in brackets in G*(t) tlϱ never reaches unity. Proposition 2 implies that the probability of an endless review is strictly positive.