Identifying probability distributions using neural networks

Economics deal with real life phenomena by constructing representative models o f the system being questioned. Input data provide the driving force for such models. The requirement o f identifying the underlying distributions of data sets is encountered in economics on numerous occasions. Most of the time, after the collection o f the raw data, the underlying statistical distribution is sought by the aid o f nonparametric statistical methods. At this step o f the problem, the feasibility of using neural networks for identification o f probability distributions is investigated. Also, for this purpose, a comparison with the traditional goodness o f fit tests is carried out in this study.