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Goodness-of-Fit Tests for Multiplicative Models with Dependent Data
Authors:HOLGER DETTE,JUAN CARLOS PARDO-FERNÁ  NDEZ, INGRID VAN KEILEGOM
Affiliation:Fakultät für Mathematik, Ruhr-Universität Bochum;
Departamento de Estatística e IO, Universidade de Vigo;
Institut de Statistique, Universitécatholique de Louvain
Abstract:Abstract.  Several classical time series models can be written as a regression model between the components of a strictly stationary bivariate process. Some of those models, such as the ARCH models, share the property of proportionality of the regression function and the scale function, which is an interesting feature in econometric and financial models. In this article, we present a procedure to test for this feature in a non-parametric context. The test is based on the difference between two non-parametric estimators of the distribution of the regression error. Asymptotic results are proved and some simulations are shown in the paper in order to illustrate the finite sample properties of the procedure.
Keywords:bootstrap    dependent data    error distribution    kernel smoothing    location-scale model    mixing sequence    multiplicative model    non-parametric regression
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