This paper considers the problem of testing for normality of the marginal law of univariate and multivariate stationary and weakly dependent random processes using a bootstrap-based Anderson-Darling test statistic. The finite-sample properties of the test are assessed via Monte Carlo...

This paper addresses the issue related to testing for non-linearity in economic models using new principal component based multivariate non-linearity tests. Monte Carlo results suggest that the new multivariate tests have good size and power properties even in small samples usually available in...

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...

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...

In this paper we focus on the finite sample properties of the conditional least squares (CLS) method of threshold autoregressive (TAR) parameters under the following conditions: (a) non-Gaussian model innovations; (b) two types of asymmetry (i.e. deepness and steepness) captured by TAR models....

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...