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    數量經濟與金融系列講座410期:Testing for Neglected Nonlinearity in the Conditional Quantile Using Neural Networks

      發布日期:2019-12-24  瀏覽次數:

    主題:Testing for Neglected Nonlinearity in the Conditional Quantile Using Neural Networks

    主講:Georg Keilbar,School of Business and Economics,Humboldt-Universit?t zu Berlin

    時間:2020年1月9日 10:00-11:30

    地點:復旦大學經濟學院714會議室

    摘要:We propose a specifification test for the conditional quantile by comparing a linearmodel to an alternative model represented by a neural network. The test is formulatedas a testing problem with nuisance parameters under the alternative. We considerthree test statistics, theaverage, theexponential-averageand thesupremum Waldstatistic as functions of a Wald process and derive their asymptotic distribution. Tosimulate critical values, we propose a consistent bootstrap procedure. Further, weshow the asymptotic optimality of theaverageandexponential-average Waldtestagainst local alternatives in a correctly specifified maximum likelihood setting. In anempirical application, we examine the nonlinearity of systemic risk in the frameworkof conditional value-at-risk (CoVaR).

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