Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models

This paper proposes a mixed‐frequency error correction model for possibly cointegrated non‐stationary time series sampled at different frequencies. We highlight the impact, in terms of model specification, of the choice of the particular high‐frequency explanatory variable to be included in the cointegrating relationship, which we call a dynamic mixed‐frequency cointegrating relationship. The forecasting performance of aggregated models and several mixed‐frequency regressions are compared in a set of Monte Carlo experiments. In particular, we look at both the unrestricted mixed‐frequency model and at a more parsimonious MIDAS regression. Whereas the existing literature has only investigated the potential improvements of the MIDAS framework for stationary time series, our study emphasizes the need to include the relevant cointegrating vectors in the non‐stationary case. Furthermore, it is illustrated that the choice of dynamic mixed‐frequency cointegrating relationship does not matter as long as the short‐run dynamics are adapted accordingly. Finally, the unrestricted model is shown to suffer from parameter proliferation for samples of relatively small size, whereas MIDAS forecasts are robust to over‐parameterization. We illustrate our results for the US inflation rate. Copyright © 2014 John Wiley & Sons, Ltd.     

BBS10 encodes a vertebrate-specific chaperonin-like protein and is a major BBS locus

Bardet-Biedl syndrome (BBS) is a genetically heterogeneous ciliopathy. Although nine BBS genes have been cloned, they explain only 40-50% of the total mutational load. Here we report a major new BBS locus, BBS10, that encodes a previously unknown, rapidly evolving vertebrate-specific chaperonin-like protein. We found BBS10 to be mutated in about 20% of an unselected cohort of families of various ethnic origins, including some families with mutations in other BBS genes, consistent with oligogenic inheritance. In zebrafish, mild suppression of bbs10 exacerbated the phenotypes of other bbs morphants.     

Estimating and Predicting the General Random Effects Model

This paper extends the ‘remarkable property’ of Breusch (Journal of Econometrics 1987; 36 : 383–389) and Baltagi and Li (Journal of Econometrics 1992; 53 : 45–51) to the three‐way random components framework. Indeed, like its one‐way and two‐way counterparts, the three‐way random effects model maximum likelihood estimation can be obtained as an iterated generalized least squares procedure through an appropriate algorithm of monotonic sequences of some variance components ratios, θ_i (i = 2, 3, 4). More specifically, a search over θ_iwhile iterating on the regression coefficients estimates β and the other θ_jwill guard against the possibility of multiple local maxima of the likelihood function. In addition, the derivations of related prediction functions are obtained based on complete as well as incomplete panels. Finally, an application to international trade issues modeling is presented. Copyright © 2014 John Wiley & Sons, Ltd.     

Maximum Transfer Distance Between Partitions

In this paper, we study a distance defined over the partitions of a finite set. Given two partitions P and Q, this distance is defined as the minimum number of transfers of an element from one class to another, required to transform P into Q. We recall the algorithm to evaluate this distance and we give some formulae for the maximum distance value between two partitions having exactly or at most p and q classes, for given p and q.     

Prediction in an Unbalanced Nested Error Components Panel Data Model

This paper derives the best linear unbiased predictor for an unbalanced nested error components panel data model. This predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested (hierarchical) structure. Examples include predicting student performance in a class in a school, or house prices in a neighborhood in a county or a state. Using Monte Carlo simulations, we show that this predictor is better in root mean square error performance than the usual fixed‐ or random‐effects predictors ignoring the nested structure of the data. This is applied to forecasting the productivity of public capital in the private sector using nested panel data of 48 contiguous American states. Copyright © 2013 John Wiley & Sons, Ltd.     

Evidence for an instructive mechanism of de novo methylation in cancer cells

DNA methylation has a role in the regulation of gene expression during normal mammalian development but can also mediate epigenetic silencing of CpG island genes in cancer and other diseases. Many individual genes (including tumor suppressors) have been shown to undergo de novo methylation in specific tumor types, but the biological logic inherent in this process is not understood. To decipher this mechanism, we have adopted a new approach for detecting CpG island DNA methylation that can be used together with microarray technology. Genome-wide analysis by this technique demonstrated that tumor-specific methylated genes belong to distinct functional categories, have common sequence motifs in their promoters and are found in clusters on chromosomes. In addition, many are already repressed in normal cells. These results are consistent with the hypothesis that cancer-related de novo methylation may come about through an instructive mechanism.     

Toward interoperable bioscience data

To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.     

De novo mutations in the actin genes ACTB and ACTG1 cause Baraitser-Winter syndrome

Brain malformations are individually rare but collectively common causes of developmental disabilities. Many forms of malformation occur sporadically and are associated with reduced reproductive fitness, pointing to a causative role for de novo mutations. Here, we report a study of Baraitser-Winter syndrome, a well-defined disorder characterized by distinct craniofacial features, ocular colobomata and neuronal migration defect. Using whole-exome sequencing of three proband-parent trios, we identified de novo missense changes in the cytoplasmic actin-encoding genes ACTB and ACTG1 in one and two probands, respectively. Sequencing of both genes in 15 additional affected individuals identified disease-causing mutations in all probands, including two recurrent de novo alterations (ACTB, encoding p.Arg196His, and ACTG1, encoding p.Ser155Phe). Our results confirm that trio-based exome sequencing is a powerful approach to discover genes causing sporadic developmental disorders, emphasize the overlapping roles of cytoplasmic actin proteins in development and suggest that Baraitser-Winter syndrome is the predominant phenotype associated with mutation of these two genes.