Goodness-of-Fit Tests for Multiplicative Models with Dependent Data |
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Authors: | HOLGER DETTE,JUAN CARLOS PARDO-FERNÁ NDEZ, INGRID VAN KEILEGOM |
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Affiliation: | Fakultät für Mathematik, Ruhr-Universität Bochum; Departamento de Estatística e IO, Universidade de Vigo; Institut de Statistique, Universitécatholique de Louvain |
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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. |
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Keywords: | bootstrap dependent data error distribution kernel smoothing location-scale model mixing sequence multiplicative model non-parametric regression |
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