7 research outputs found

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

    Estimation of nonstationary heterogeneous panels

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    We introduce a new Stata command, xtpmg, for estimating nonstationary heterogeneous panels in which the number of groups and number of time-series observations are both large. Based on recent advances in the nonstationary panel literature, xtpmg provides three alternative estimators: a traditional fixed-effects estimator, the mean-group estimator of Pesaran and Smith (Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics 68: 79–113), and the pooled mean-group estimator of Pesaran, Shin, and Smith (Estimating long-run relationships in dynamic heterogeneous panels, DAE Working Papers Amalgamated Series 9721; Pooled mean group estimation of dynamic heterogeneous panels, Journal of the American Statistical Association 94: 621–634)

    Estimation of nonstationary heterogeneous panels

    No full text
    We introduce a new Stata command, xtpmg, for estimating nonstationary heterogeneous panels in which the number of groups and number of time-series observations are both large. Based on recent advances in the nonstationary panel literature, xtpmg provides three alternative estimators: a traditional fixed-effects estimator, the mean-group estimator of Pesaran and Smith (Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics 68: 79-113), and the pooled mean-group estimator of Pesaran, Shin, and Smith (Estimating long-run relationships in dynamic heterogeneous panels, DAE Working Papers Amalgamated Series 9721; Pooled mean group estimation of dynamic heterogeneous panels, Journal of the American Statistical Association 94: 621-634). Copyright 2007 by StataCorp LP.xtpmg, nonstationary panels, heterogeneous dynamic panels, pooled mean-group estimator, mean-group estimator, panel cointegration

    XTPMG: Stata module for estimation of nonstationary heterogeneous panels

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    We introduce a new Stata command, xtpmg, for estimating nonstationary heterogeneous panels in which the number of groups and number of time-series observations are both large. Based on recent advances in the nonstationary panel literature, xtpmg provides three alternative estimators: a traditional fixed-effects estimator, the mean-group estimator of Pesaran and Smith (Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics 68: 79-113), and the pooled mean-group estimator of Pesaran, Shin, and Smith (Estimating long-run relationships in dynamic heterogeneous panels, DAE Working Papers Amalgamated Series 9721; Pooled mean group estimation of dynamic heterogeneous panels, Journal of the American Statistical Association 94: 621-634). This routine is described in Stata Journal, 2007, 7:2, 197-208.xtpmg, nonstationary panels, heterogeneous dynamic panels, pooled mean-group estimator, mean-group estimator, panel cointegration
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