Distributional assumptions are crucial in the estimation of the value of public projects assessed by means of contingent valuation analyses, and it would seem obvious that tests for model specification should play an important part in the statistical analysis. It can be observed, though, that when the competing hypotheses are non nested, the choice of the model is often based on heuristic grounds, or, at most, on deterministic selection model criteria such as Akaike's (1973). In this paper we study two alternative, probabilistic, approaches to checking model specification, that, like Akaike's, are based on the Kullback-Leibler Information Criterion (KLIC): the model selection testing proposed by Vuong (1989) and the non nested model test proposed by Cox, in the simulated approach of Pesaran and Pesaran (1993). The three approaches are confronted by comparing their performance in selecting among different models applied to simulated contingent valuation data. Our results seem to warrant the use of the Cox test for medium-large size samples, while for small size samples its performance is less satisfactory. When the data set is small, use of a model selection method may be preferred to model testing. In this case, the Vuong model selection testing is recommended as an alternative to the deterministic approach of the Akaike's criterion.
Contingent valuation maximum likelihood non nested models model selection model testing