This paper consider the GLS detrending procedure advanced by Elliott et al. (1996) for unit root tests against alternative hypotheses where the time series data under investigation follow either globally stationary SETAR or STAR processes with deterministic components being present. It is found...

Most work in the area of nonlinear econometric modelling is based on a single equation and assumes exogeneity of the explanatory variables. Recently, work by Caner and Hansen (2003) and Psaradakis, Sola, and Spagnolo (2004) has considered the possibility of estimating nonlinear models by methods...

In this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is...

This paper constructs tests for the presence of nonlinearity of unknown form in addition to a fractionally integrated, long memory component in a time series process. The tests are based on artificial neural network structures and do not restrict the parametric form of the nonlinearity. The...

Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little...

This paper investigates GLS detrending procedures for unit root tests against nonlinear stationary alternative hypotheses where deterministic components are assumed present in the series under investigation. It is found that the proposed procedures have considerable power gains in a majority of...

This paper considers estimation and inference in some general non linear time series models which are embedded in a strongly dependent, long memory process. Some new results are provided on the properties of a time domain MLE for these models. The paper also includes a detailed simulation study...

Interest in the interface of nonstationarity and nonlinearity has been increasing in the econometric literature. The motivation for this development maybe be traced to the perceived possibility that processes following nonlinear models maybe mistakenly taken to be unit root or long-memory...

This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the...

This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are...