Dynamic Factors in the Presence of Block Structure

Macroeconometric data often come under the form of large panels of time series, themselves decomposing into smaller but still quite large subpanels or blocks. We show how the dynamic factor analysis method proposed in Forni et al (2000), combined with the identification method of Hallin and Liska (2007), allows for identifying and estimating joint and block-specific common factors. This leads to a more sophisticated analysis of the structures of dynamic interrelations within and between the blocks in such datasets, along with an informative decomposition of explained variances. The method is illustrated with an analysis of the Industrial Production Index data for France, Germany, and Italy.Panel data; Time series; High dimensional data; Dynamic factor model; Business cycle; Block specific factors; Dynamic principal components; Information criterion.

The Internal Market Index 2004

Abstract not availableJRC.G-Institute for the Protection and the Security of the Citizen (Ispra

Validation Study Report: Performance assessment of the AR-CALUX® in vitro method: to support the development of an international test guideline for Androgen Receptor Transactivation Assays (ARTA) for the detection of compounds with (anti)androgenic potential

The JRC’s EU Reference Laboratory for alternatives to animal testing (EURL ECVAM) conducted a validation study of the AR-CALUX in vitro method. The method is applied to the detection of compounds with endocrine disrupting potential and more specifically (anti)androgen activity. The objectives of the study included assessing the reproducibility (within and between laboratories) and the relevance of the in vitro method. The participating laboratories included three test facilities from the European Union Network of Validation Laboratories for alternative methods (EU-NETVAL), being RISE, Covance, and Charles River, and, the test method submitter Biodetection Systems. The validation study report presents the results of the method performed on 46 test chemicals, evaluated for reproducibility within and between laboratories, variability within the measurements, and classification. A comparison of the classifications was made with publicly available ARTA classifications. It was concluded that this test method is reliable, has high reproducibility, low variability and merits proposal to OECD for the development of a test guideline. The statistical report and the final SOP are part of this report and can be found as separate files.JRC.F.3-Chemicals Safety and Alternative Method

Problems in Cognitive-Behavioral Supervision : Theoretical Background and Clinical Application

Cognitive-behavioural therapists and trainees are encouraged to undergo supervision when offering therapy to troubled clients and to process personal attitudes and events likely to affect their therapeutic work. We discuss common problems in cognitive behavioural therapy (CBT) supervision, which may arise at the client, supervisee, or supervisor level. These issues include difficulties with case formulation, therapeutic strategies, and the therapeutic relationship. A supervisor can help their supervisee deal with clients with multifaceted or particularly challenging problems, such as difficulties with compliance, complex psychosocial problems, or chronic mental disorders. We also discuss matters related to the supervision process, the supervisor's role, different supervisory styles, and issues affecting a supervisee's feelings of vulnerability and shame. Furthermore, we analyze distinct supervision styles and potential problems arising from the supervision of experienced CBT therapists.Peer reviewe

Whole Genome Sequences of Three Treponema pallidum ssp. pertenue Strains: Yaws and Syphilis Treponemes Differ in Less than 0.2% of the Genome Sequence

Spirochete Treponema pallidum ssp. pertenue (TPE) is the causative agent of yaws while strains of Treponema pallidum ssp. pallidum (TPA) cause syphilis. Both yaws and syphilis are distinguished on the basis of epidemiological characteristics and clinical symptoms. Neither treponeme can reproduce outside the host organism, which precludes the use of standard molecular biology techniques used to study cultivable pathogens. In this study, we determined high quality whole genome sequences of TPE strains and compared them to known genetic information for T. pallidum ssp. pallidum strains. The genome structure was identical in all three TPE strains and also between TPA and TPE strains. The TPE genome length ranged between 1,139,330 bp and 1,139,744 bp. The overall sequence identity between TPA and TPE genomes was 99.8%, indicating that the two pathogens are extremely closely related. A set of 34 TPE genes (3.5%) encoded proteins containing six or more amino acid replacements or other major sequence changes. These genes more often belonged to the group of genes with predicted virulence and unknown functions suggesting their involvement in infection differences between yaws and syphilis

Three Essays on Spectral Analysis and Dynamic Factors

The main objective of this work is to propose new procedures for the general dynamic factor analysis introduced by Forni et al. (2000). First, we develop an identification method for determining the number of common shocks in the general dynamic factor model. Suficient conditions for consistency of the criterion are provided for large n (number of series) and T (the series length). We believe that our procedure can shed light on the ongoing debate on the number of factors driving the US or Eurozone economy. Second, we show how the dynamic factor analysis method proposed in Forni et al. (2000), combined with our identification method, allows for identifying and estimating joint and block-specific common factors. This leads to a more sophisticated analysis of the structures of dynamic interrelations within and between the blocks in such datasets. Besides the framework of the general dynamic factor model we also propose a consistent lag window spectral density estimator based on multivariate M-estimators by Maronna (1976) when the underlying data are coming from the alpha mixing stationary Gaussian process.JRC.G.9-Econometrics and statistical support to antifrau

The generalized dynamic factor model: determining the number of factors

info:eu-repo/semantics/publishe

Dynamic Factors in the Presence of Block Structure

Macroeconometric data often come under the form of large panels of time series, themselves decomposing into smaller but still quite large subpanels or blocks. We show how the dynamic factor analysis method proposed in Forni et al (2000), combined with the identification method of Hallin and Liska (2007), allows for identifying and estimating joint and block-specific common factors. This leads to a more sophisticated analysis of the structures of dynamic interrelations within and between the blocks in such datasets, along with an informative decomposition of explained variances. The method is illustrated with an analysis of the Industrial Production Index data for France, Germany, and Italy.info:eu-repo/semantics/publishe