| Abstract: | Very often, one needs to perform (classical or Bayesian) inference, when essentially nothing is known about the distribution of the dependent variable given certain covariates. The paper proposes to approximate the unknown distribution by its non-parametric counterpart—a step function—and treat the points of the support and the corresponding density values, as parameters, whose posterior distributions should be determined based on the available data. The paper proposes distributions should be determined based on the available data. The paper proposes Markov chain Monte Carlo methods to perform posterior analysis, and applies the new method to an analysis of stock returns. Copyright © 1999 John Wiley & Sons, Ltd. |
