Background: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach

We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based on Shenton and Bowman [Journal of the American Statistical Association (1977) Vol. 72, pp. 206-211], which controls well for size, for samples as low as 10 observations. A multivariate version is introduced. Size and power are investigated in comparison with four other tests for multivariate normality. The first power experiments consider the whole skewness-kurtosis plane; the second use a bivariate distribution which has normal marginals. It is concluded that the proposed test has the best size and power properties of the tests considered.

This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross-country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on 'good' policy. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes. AbstractThis paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross-country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on 'good' policy. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.JEL classification: O1; O2; O4; C23

The present paper re-examines the effectiveness of foreign aid theoretically and empirically. Using a standard OLG model we show that aid inflows will in general affect long-run productivity. The size and direction of the impact may depend on policies, 'deep' structural characteristics and the size of the inflow. The empirical analysis investigates these possibilities. Overall we find that aid has been effective in spurring growth, but the magnitude of the effect depends on climate-related circumstances. Finally, we argue that the Collier-Dollar allocation rule should be seriously reconsidered by donor agencies if aid effectiveness is related to climate.

Some methods for the evaluation of parameter constancy in cointegrated vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VARmodel are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and another in which the cointegrating relations are estimated recursively from a likelihood function, where the short-run parameters have been concentrated out. We suggest graphical procedures based on recursively estimated eigenvalues to evaluate the constancy of the long-run parameters in the model. Specifically, we look at the time paths of the eigenvalues using a new result on the asymptotic distribution of the estimated eigenvalues. Furthermore, we show that the fluctuation test by Ploberger et al. (1989) and the Lagrange multiplier (LM) type test for constancy of parameters by Nyblom (1989) can be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities.