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This article addresses the contribution to hedonic modeling of a nonparametric approach based on artificial neural network (ANN) regressions. ANNs provide consistent estimates for the hedonic price of each attribute and permit a number of hypotheses on the hedonic price relationship to be tested nonparametrically. In particular, we exploit results by Stinchcombe and White (Econom Theory 14:295-324, 1998) in order to carry out misspecification testing in linear and semiloglinear hedonic models. The same approach directly applies to testing misspecification of any parametric specification for the hedonic relationship. A nonparametric significance test for the variables in the hedonic model is also proposed. The test extends the approach developed by Racine (J Bus Econ Stat 15(3):369-378, 1997) in kernel-based nonparametric testing to ANN-based inference. The finite sample performance of the proposed tests is analyzed through Monte Carlo experiments, and simulation-based algorithms for computation of the null distribution of the tests are proposed. Then, the performance of three classes of regression models-linear, semi-log, and ANNs-applied to Electronic supplementary material The online version of this article (123 988 M. Landajo et al.hedonic price modeling in a Spanish regional housing market is compared. Our results indicate the presence of nonlinear behavior, as predicted by economic theory, with the ANN-based tests detecting statistically significant evidence of misspecification-both in the linear and the semilog specifications-and ANN regressions providing moderate improvement of predictive performance.

In this paper, a Goal Programming model is developed in order to study the possibility of decreasing the length of stay on the waiting list of a hospital that belongs to the Spanish Health Service. First, a problem is solved to determine the optimal planning for one year, so as to make the maximum waiting time decrease to six months (at present, some operations have a waiting list of more than a year). Afterwards, two other problems are solved in order to determine the impact that a further reduction of the waiting time (four months) would have on the requirements of extra resources for the hospital. The particular problem for the Trauma service is described in detail, but global results are shown and commented.

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