Two well known multivariate non-linearity tests are modified using a principal component analysis. The Monte Carlo results show that the proposed principal component-based tests do provide a remarkable dimensionality reduction without any systematic power loss. It can be concluded that using...

This survey focuses on two families of nonlinear vector time series models, the family of Vector Threshold Regression models and that of Vector Smooth Transition Regression models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in...

A new version of the Q test, based on generalized residual correlations (i.e. auto-correlations and cross-correlations), is developed in this paper. The Q test fixes two main shortcomings of the Mcleod and Li Q (MLQ) test often used in the literature: (i) the test is capable to capture some...

The paper examines the robustness of the size and power properties of the standard non-linearity tests under different conditions such as moment failure and asymmetry of innovations. Our results reveal the following. First, there seems not to be a direct link between moment condition failure and...

Determining good parameter estimates in ESTAR models is known to be diffcult. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identifcation problem, the problem of properly distinguishing the transition function in relation to extreme parameter...

Determining good parameter estimates in ESTAR models is known to be diffcult. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identifcation problem, the problem of properly distinguishing the transition function in relation to extreme parameter...