Search

© 2017 The Ohio State University We compare a number of widely used trend-cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions—different decompositions have broad implications for the relative importance of real versus nominal shocks in explaining variations in output. Using U.S. quarterly real GDP, we find that the overall best model is an unobserved components model with two features: (i) a nonzero correlation between trend and cycle innovations and (ii) a break in trend output growth in 2007. The annualized trend output growth decreases from about 3.4% to 1.2%–1.5% after the break. The results also indicate that real shocks are more important than nominal shocks. The slowdown in trend output growth is robust when we expand the set of models to include bivariate unobserved components models.

© 2017 Elsevier B.V. This paper reconciles two widely used trend–cycle decompositions of GDP that give markedly different estimates: the correlated unobserved components model yields output gaps that are small in amplitude, whereas the Hodrick–Prescott (HP) filter generates large and persistent cycles. By embedding the HP filter in an unobserved components model, we show that this difference arises due to differences in the way the stochastic trend is modeled. Moreover, the HP filter implies that the cyclical components are serially independent—an assumption that is decidedly rejected by the data. By relaxing this restrictive assumption, the augmented HP filter provides comparable model fit relative to the standard correlated unobserved components model.

© The Author, 2016. Published by Oxford University Press. All rights reserved. We propose importance sampling algorithms based on fast band matrix routines for estimating the observed-data likelihoods for a variety of stochastic volatility models. This is motivated by the problem of computing the deviance information criterion (DIC)-a popular Bayesian model comparison criterion that comes in a few variants. Although the DIC based on the conditional likelihood-obtained by conditioning on the latent variables-is widely used for comparing stochastic volatility models, recent studies have argued against its use on both theoretical and practical grounds. Indeed, we show via a Monte-Carlo study that the conditional DIC tends to favor overfitted models, whereas the DIC based on the observed-data likelihood-calculated using the proposed importance sampling algorithms-seems to perform well. We demonstrate the methodology with an application involving daily returns on the Standard & Poors 500 index.

© 2015 Elsevier B.V. We compare a number of GARCH and stochastic volatility (SV) models using nine series of oil, petroleum product and natural gas prices in a formal Bayesian model comparison exercise. The competing models include the standard models of GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, and t distributed and moving average innovations. We find that: (1) SV models generally compare favorably to their GARCH counterparts; (2) the jump component and t distributed innovations substantially improve the performance of the standard GARCH, but are unimportant for the SV model; (3) the volatility feedback channel seems to be superfluous; (4) the moving average component markedly improves the fit of both GARCH and SV models; and (5) the leverage effect is important for modeling crude oil prices-West Texas Intermediate and Brent-but not for other energy prices. Overall, the SV model with moving average innovations is the best model for all nine series.

© 2014 Elsevier B.V. The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of certain variants of the DIC for comparing latent variable models. For example, it has been argued that the conditional DIC–based on the conditional likelihood obtained by conditioning on the latent variables–is sensitive to transformations of latent variables and distributions. Further, in a Monte Carlo study that compares various Poisson models, the conditional DIC almost always prefers an incorrect model. In contrast, the observed-data DIC–calculated using the observed-data likelihood obtained by integrating out the latent variables–seems to perform well. It is also the case that the conditional DIC based on the maximum a posteriori (MAP) estimate might not even exist, whereas the observed-data DIC does not suffer from this problem. In view of these considerations, fast algorithms for computing the observed-data DIC for a variety of high-dimensional latent variable models are developed. Through three empirical applications it is demonstrated that the observed-data DICs have much smaller numerical standard errors compared to the conditional DICs. The corresponding MATLAB code is available upon request.

© 2015 Elsevier B.V. The modified harmonic mean is widely used for estimating the marginal likelihood. We investigate the empirical performance of two versions of this estimator: one based on the observed-data likelihood and the other on the complete-data likelihood. Through an empirical example using US and UK inflation, we show that the version based on the complete-data likelihood has a substantial bias and tends to select the wrong model, whereas the version based on the observed-data likelihood works well.

Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterised by tumours of the parathyroids, pancreas and anterior pituitary that represents one of the familial cancer syndromes. The MEN1 locus has been previously localised to chromosome 11q13, and a < 300 kb gene-rich region flanked centromerically by PYGM and telomerically by D11S1783 defined by combined meiotic and tumour deletion mapping studies. Two candidate genes, ZFM1 and PPP2R5B,from this region have been previously excluded, and in order to identify additional candidate genes we used a BAC to isolate cDNAs from a bovine parathyroid cDNA library by direct selection. One of the novel genes that we identified, SCG2, proved to be identical to the recently published MEN1 gene, which is likely to be a tumour suppressor gene. The SCG2 transcript was 2.9 kb in all tissues with an additional 4.2 kb transcript also being present in the pancreas and thymus. Mutational analysis of SCG2 in 10 unrelated MEN1 families identified one polymorphism and nine different heterozygous mutations (one missense, four non-sense, one insertional and three deletional frameshifts) that segregated with the disease, hence providing an independent confirmation for the identification of the MEN1 gene.

Background: Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population. Methods: The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992–2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed. Results: The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell’s C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264–0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084–0.575)). Conclusions: LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level., German Research Foundation (DFG) AL 1784/3-1, European Commission European Commission Joint Research Centre, International Agency for Research on Cancer, Danish Cancer Society, Ligue Contre le Cancer (France), Institut Gustave Roussy (France), Mutuelle Generale de l'Education Nationale (France), Institut National de la Sante et de la Recherche Medicale (Inserm), Deutsche Krebshilfe, Deutsches Krebsforschungszentrum (DKFZ) (Germany), Federal Ministry of Education & Research (BMBF), Hellenic Health Foundation (Greece), Associazione Italiana per la Ricerca sul Cancro (AIRC), Consiglio Nazionale delle Ricerche (CNR), Netherlands Government, World Cancer Research Fund International (WCRF), Instituto de Salud Carlos III PI13/00061 PI13/01162, Junta de Andalucia, Regional Government of Asturias (Spain), Regional Government of Basque Country (Spain), Regional Government of Murcia (Spain) 6236, Regional Government of Navarra (Spain), Regional Government of Catalonia (Catalan Institute of Oncology -ICO-IDIBELL) (Spain), Swedish Cancer Society, Swedish Research Council, County Council of Skane (Sweden), County Council of Vasterbotten (Sweden), Cancer Research UK C864/A14136 C8221/A19170, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/N003284/1 MC-UU_12015/1 MR/M012190/1, Projekt DEAL

The optimization of conditions to promote the stemness of pluripotent cells in vitro is instrumental for their use in advanced therapies. We show here that exposure of human iPSCs and human ESCs to low concentrations of the chemical NO donor DETA/NO leads to stabilization of hypoxia-inducible factors (HIF-1α and HIF-2α) under normoxia, with this effect being dependent on diminished Pro 402 hydroxylation and decreased degradation by the proteasome. Moreover, the master genes of pluripotency, NANOG and OCT-4, were upregulated. NO also induces a shift in the metabolic profile of PSCs, with an increased expression of hypoxia response genes in glycolysis. Furthermore, a reduction in the mitochondrial membrane potential with lower oxygen consumption and increased expression of mitochondrial fusion regulators, such as DRP1, was observed. The results reported here indicate that NO mimics hypoxia response in human PSCs and enhances their stemness properties when cultured under normoxic conditions.