In this note we establish the existence of the first two moments of the asymptotic trace statistic, which appears as weak limit of the likelihood ratio statistic for testing the cointe- gration rank in a vector autoregressive model and whose moments may be used to develop panel cointegration...

In regression analysis there is typically a large collection of competing models available from which we want to select an appropriate one. This paper is concerned with asymptotic properties of procedures for selecting linear models, which are based on certain data-dependent criteria such as...

A new likelihood-based panel cointegration test which allows a linear time trend in the data generating process is proposed. The test is an extension of the likelihood ratio type test with trend adjustment prior to testing to the panel data framework. Under the null hypothesis, the standardized...

We examine the impact of performance groups on the estimation of the relative importance of firm, industry and other effects on corporate performance. Performance groups comprise firms from the same industry with a similar performance over a longer period of time. We present a statistical method...

For the problem of model selection, full cross-validation has been proposed as alternative criterion to the traditional cross-validation, particularly in cases where the latter one is not well defined. To justify the use of the new proposal we show that under some conditions, both criteria share...

Consider estimating the mean of a normal distribution with known variance, when that mean is known to lie in a bounded interval. In a decision-theoretic framework we study finite sample properties of a class of nonlinear' estimators. These estimators are based on thresholding techniques which...

The results of analyzing experimental data using a parametric model may heavily depend on the chosen model. In this paper we propose procedures for the adequate selection of nonlinear regression models if the intended use of the model is among the following: prediction of future values of the...