This article develops an information criterion for determining the number q of common shocks in the general dynamic factor model developed by Forni et al., as opposed to the restricted dynamic model considered by Bai and Ng and by Amengual and Watson. Our criterion is based on the fact that this number q is also the number of diverging eigenvalues of the spectral density matrix of the observations as the number n of series goes to infinity. We provide sufficient conditions for consistency of the criterion for large n and T (where T is the series length). We show how the method can be implemented and provide simulations and empirics illustrating its very good finite-sample performance. Application to real data adds a new empirical facet to an ongoing debate on the number of factors driving the U.S. economy.

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Composite indicators are regularly used for benchmarking countries' performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of subindicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries' composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration. * This paper is an offshoot of the KEI-project (contract n° 502529) that is part of priority 8 of the policy orientated research under the European Commission's Sixth Framework Programme (see http://kei.publicstatistics.net/). Laurens Cherchye thanks the Fund for Scientific Research-Flanders (FWO-Vlaanderen) for his postdoctoral fellowship.

CYP enzyme induction is a sensitive biomarker for phenotypic metabolic competence of in vitro test systems; it is a key event associated with thyroid disruption, and a biomarker for toxicologically relevant nuclear receptor-mediated pathways. This paper summarises the results of a multi-laboratory validation study of two in vitro methods that assess the potential of chemicals to induce cytochrome P450 (CYP) enzyme activity, in particular CYP1A2, CYP2B6, and CYP3A4. The methods are based on the use of cryopreserved primary human hepatocytes (PHH) and human HepaRG cells. The validation study was coordinated by the European Union Reference Laboratory for Alternatives to Animal Testing of the European Commission's Joint Research Centre and involved a ring trial among six laboratories. The reproducibility was assessed within and between laboratories using a validation set of 13 selected chemicals (known human inducers and non-inducers) tested under blind conditions. The ability of the two methods to predict human CYP induction potential was assessed. Chemical space analysis confirmed that the selected chemicals are broadly representative of a diverse range of chemicals. The two methods were found to be reliable and relevant in vitro tools for the assessment of human CYP induction, with the HepaRG method being better suited for routine testing. Recommendations for the practical application of the two methods are proposed.

Composite indicators are regularly used for benchmarking countries' performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of subindicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries' composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration. * This paper is an offshoot of the KEI-project (contract n° 502529) that is part of priority 8 of the policy orientated research under the European Commission's Sixth Framework Programme (see http://kei.publicstatistics.net/). Laurens Cherchye thanks the Fund for Scientific Research-Flanders (FWO-Vlaanderen) for his postdoctoral fellowship.