The results of analyzing experimental data using a parametric approach may heavily depend on the chosen model. With this paper we describe computational tools in Splus for a simultaneous selection of parametric regression and variance models from a relatively rich model class and of Box-Cox...

In the present paper we confine ourselves to proposing tests for smooth transition nonlinearity in the presence ou outliers. We consider outlier robust estimation techniques to modify the tests developed by Luukkonen et al

Repeated-imputation inference (RII) techniques for estimating nonlinear models with multiply imputed data are described. RII techniques are used to estimate a logit model using the 1995 Survey of Consumer Finances. RII techniques use all information available in multiply imputed data and...

The book deals with the econometric analysis of high frequency financial time series. It emphasizes a new nonparametric approach to volatility models and provides theoretical and empirical comparisons with conventional ARCH models, applied to foreign exchange rates. Nonparametric models are...

This paper tests for nonlinearity in EMS exchange rates using the bispectrum. The early experience of the ERM witnessed numerous realignments. We find that exchange rates follow a linear process over the period 1979-1987, consistent with the predictions of the realignment target zone model,...

In this paper I explore the issue of nonlinearity (both in the data generation process and in the functional form that establishes the relationship between the parameters and the data) regarding the poor performance of the Generalized Method of Moments (GMM) in small samples. To this purpose I...

This paper considers smooth transition regression models and their univariate counterparts, smooth transition autoregressive models. The model is defined and thereafter, linearity testing, statistical inference in smooth transition models, and areas of application are discussed. A bivariate...

A general limit theorem is established for time series regression estimates which include generalized least squares, in the presence of long range dependence in both errors and stochastic regressors. The setting and results differ significantly from earlier work on regression with long range...