Distribution free goodness-of-fit tests for linear processes

By Miguel A. Delgado, Javier Hidalgo, Carlos Velasco. 2006

73 citations recorded by the source corpus.

This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence. Test statistics for composite hypotheses are functionals of a (approximated) martingale transformation of the Bartlett's Tp-process with estimated parameters, which converges in distribution to the standard Brownian Motion under the null hypothesis. We discuss tests of different nature such as omnibus, directional and Portmanteau-type tests. A Monte Carlo study illustrates the performance of the different tests in practice.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a8e36a126557e905b050355dce07d15ea75573b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Wenceslao González-Manteiga
2. Michael Eichler
3. Rosa M. Crujeiras
4. Holger Dette
5. Efstathios Paparoditis

In Finance

1. Wenceslao González-Manteiga
2. Michael Eichler
3. Rosa M. Crujeiras

Citations in the corpus

• Testing nonparametric and semiparametric hypotheses in vector stationary processes (2008, background): “, [41,60,15]), which usually get by with moments of fourth or eighth order, our approach is not restricted to linear processes.”
• An updated review of Goodness-of-Fit tests for regression models (2013, background): “See Delgado et al. (2005), Hidalgo and Kreiss (2006), and Delgado and Velasco (2010).”