nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2008‒06‒13
two papers chosen by
Philip Yu
Hong Kong University

  1. When is some number really better than no number? On the optimal choice between non-market valuation methods By Munro, Alistair
  2. Asymptotic and bootstrap properties of rank regressions By Subbotin, Viktor

  1. By: Munro, Alistair
    Abstract: Decision-makers have a wide variety of competing and complementary methods for non-market valuation, but there is little formal advice on the choice of method. I offer a formal approach, using a loss function (the mean square error) to compare contingent valuation, Citizens'Jury and methods where by intention only a portion of total value is estimated, when a) preferences vary across the population and b) methods are more or less susceptible to framing effects. Illustrative simulations suggest con-ditions under which the Citizens'Jury may dominate contingent valuation when framing effects are significant.
    Keywords: contingent valuation; Citizens' Jury; optimal decisions; framing effects; cost-benefit analysis
    JEL: D61 Q51 D01
    Date: 2007–09–20
  2. By: Subbotin, Viktor
    Abstract: The paper develops the bootstrap theory and extends the asymptotic theory of rank estimators, such as the Maximum Rank Correlation Estimator (MRC) of Han (1987), Monotone Rank Estimator (MR) of Cavanagh and Sherman (1998) or Pairwise-Difference Rank Estimators (PDR) of Abrevaya (2003). It is known that under general conditions these estimators have asymptotic normal distributions, but the asymptotic variances are difficult to find. Here we prove that the quantiles and the variances of the asymptotic distributions can be consistently estimated by the nonparametric bootstrap. We investigate the accuracy of inference based on the asymptotic approximation and the bootstrap, and provide bounds on the associated error. In the case of MRC and MR, the bound is a function of the sample size of order close to n^{-1/6}. The PDR estimators belong to a special subclass of rank estimators for which the bound is vanishing with the rate close to n^{-1/2}. The theoretical findings are illustrated with Monte-Carlo experiments and a real data example.
    Keywords: Rank Estimators; Bootstrap; M-Estimators; U-Statistics; U-Processes
    JEL: C14 C12 C15
    Date: 2007–11–08

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