Stochastic Trends, Deterministic Trends, and Business Cycle Turning Points
This study examines the relationship between specifications for long-run output patterns and specifications for business cycle dynamics. In an application to US GDP, it is found that inferences about the nature of the trend in output are not robust to changes in the specification for short-run fluctuations. Similarly, the choice of which model best describes the transitory movements in output depends on the way in which the trend is specified. The empirical analysis makes use of Bayesian methods to compare time series models for US GDP. Inspection of the predictive densities for the individual data points suggests that the information contained in the data is largely limited to the observations associated with business cycle turning points (c) 1997 John Wiley & Sons, Ltd.
SUMMARY ::: One major challenge of the post-sequencing era of the human genome project will be the functional annotation of the non-coding portion of the genome, in particular gene regulatory sequences. We have developed a new web-based tool, TFBScluster, which performs genome-wide identification of transcription factor binding site clusters that are conserved in multiple mammalian genomes. Clusters representing candidate gene regulatory elements can be filtered further, based on the presence or absence of additional user-defined DNA sequence motifs or by constraining the orientation or order of binding sites. Comprehensive results files, returned by email, are designed to facilitate experimental validation of computationally identified candidate gene regulatory sequences. TFBScluster, therefore, has the potential to contribute to deciphering transcriptional networks that regulate a wide range of mammalian developmental processes.