Minimum chi-square estimation and tests for model selection

Quang Vuong, Weiren Wang

    Research output: Contribution to journalArticle

    Abstract

    This paper studies the problem of model selection based on Pearson chi-square type statistics. Such goodness-of-fit statistics have been considered by Moore (1978) and diagnostic tests based on them have recently been extended to general econometric models by Andrews (1988a, b). Here we consider another important use of these statistics. Specifically, we propose some convenient asymptotically standard normal tests for model selection based on chi-square type statistics. Following Vuong (1989), the null hypothesis is that the competing models are equally close to the data-generating process (DGP) vs. the alternative hypotheses that one model is closer to the DGP where closeness of a model is measured according to the discrepancy implicit in the Pearson type statistic used. Our model selection tests have the desirable feature that neither model needs to be correctly specified nor nested in each other. As a prerequisite, we study the corresponding class of minimum chi-square estimators.

    Original languageEnglish (US)
    Pages (from-to)141-168
    Number of pages28
    JournalJournal of Econometrics
    Volume56
    Issue number1-2
    DOIs
    Publication statusPublished - 1993

      Fingerprint

    ASJC Scopus subject areas

    • Economics and Econometrics
    • Finance
    • Statistics and Probability

    Cite this