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
In the common non-parametric regression model the problem of testing for the parametric form of the ...
summary:Test procedures are constructed for testing the goodness-of-fit in parametric regression mod...
This paper proposes a test statistic for discriminating between two partly non-linear regression m...
Several classical time series models can be written as a regression model of the form Yt = m(Xt ) + ...
International audienceThe objective is to construct a tool to test the validity of a regression mode...
For the heteroscedastic nonparametric regression model Yni = m(xni)+σ(xni)Є ni; i = 1; ...; n; we pr...
Consider the nonparametric regression model Y = m(X) + ε, where the function m is smooth, but unknow...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
Mixed models, with both random and fixed effects, are most often estimated on the assump-tion that t...
We consider a goodness-of-fit test for certain parametrizations of conditionally heteroscedastic tim...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...
Several new tests are proposed for examining the adequacy of a family of parametric models against l...
This paper proposes several tests of restricted specification in nonparametric instrumental regressi...
Several diagnostic tests for the lack of fit time series models have been introduced using parametri...
We address the problem of checking the adequacy of a parametric functional form for the fixed effect...
In the common non-parametric regression model the problem of testing for the parametric form of the ...
summary:Test procedures are constructed for testing the goodness-of-fit in parametric regression mod...
This paper proposes a test statistic for discriminating between two partly non-linear regression m...
Several classical time series models can be written as a regression model of the form Yt = m(Xt ) + ...
International audienceThe objective is to construct a tool to test the validity of a regression mode...
For the heteroscedastic nonparametric regression model Yni = m(xni)+σ(xni)Є ni; i = 1; ...; n; we pr...
Consider the nonparametric regression model Y = m(X) + ε, where the function m is smooth, but unknow...
AbstractWe propose a natural test of fit of a parametric regression model. The test is based on a co...
Mixed models, with both random and fixed effects, are most often estimated on the assump-tion that t...
We consider a goodness-of-fit test for certain parametrizations of conditionally heteroscedastic tim...
General methods for testing the fit of a parametric function are proposed. The idea underlying each ...
Several new tests are proposed for examining the adequacy of a family of parametric models against l...
This paper proposes several tests of restricted specification in nonparametric instrumental regressi...
Several diagnostic tests for the lack of fit time series models have been introduced using parametri...
We address the problem of checking the adequacy of a parametric functional form for the fixed effect...
In the common non-parametric regression model the problem of testing for the parametric form of the ...
summary:Test procedures are constructed for testing the goodness-of-fit in parametric regression mod...
This paper proposes a test statistic for discriminating between two partly non-linear regression m...