240 research outputs found
Solid-state NMR study of the transformation of octacalcium phosphate to hydroxyapatite: A mechanistic model for central dark line formation
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Parallel beta-sheets and polar zippers in amyloid fibrils formed by residues 10-39 of the yeast prion protein Ure2p
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Jackie Chan: A New Dragon for a New Generation
No full textJackie Chan is a dragon of the Hong Kong cinema. Certain life experiences mould his star image as narrative, such as his childhood operatic training, his cheeky brand of kung fu comedy, and the high-risk onscreen stunts that he performs himself. But star images are made, not born. This essay looks at Jackie Chan's rise to stardom through two key texts: the film Drunken Master (Zuiquan, 1978) and his English language autobiography, I am Jackie Chan (1998). Both narrate a rite-of-passage story that defines Chan on and off the screen in terms of a painful transition from kung fu kid to dragon through his operatic training and translated into martial arts lessons onscreen. This training is crucial to his brand of comic genius. Chan acknowledges these aspects of his image when he sees his operatic training - ten years of hell - as the foundation of his stardom. He writes that his blood father is 'the father of Chan Kong-sang' but his opera school Master is 'the father of Jackie Chan'.Griffith Business School, Department of International Business and Asian StudiesNo Full Tex
Genetic aberrations in chronic lymphocytic leukaemia as prognostic markers
No full textpublished_or_final_versionPathologyMasterMaster of Philosoph
On the observed-data deviance information criterion for volatility modeling
No full text© 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
Moving average stochastic volatility models with application to inflation forecast
No full textWe introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in precision-based algorithms for estimating these new models. In an empirical application involving US inflation we find that these moving average stochastic volatility models provide better in-sample fitness and out-of-sample forecast performance than the standard variants with only stochastic volatility. © 2013 Elsevier B.V. All rights reserved
The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling
No full text© 2017 American Statistical Association. This article generalizes the popular stochastic volatility in mean model to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance, and its coefficient in the former is time-varying. We develop an efficient Markov chain Monte Carlo algorithm based on band and sparse matrix algorithms instead of the Kalman filter to estimate this more general variant. The methodology is illustrated with an application that involves U.S., U.K., and Germany inflation. The estimation results show substantial time-variation in the coefficient associated with the volatility, highlighting the empirical relevance of the proposed extension. Moreover, in a pseudo out-of-sample forecasting exercise, the proposed variant also forecasts better than various standard benchmarks
Large Bayesian Vector Autoregressions
Get PDF© 2020, Springer Nature Switzerland AG. Bayesian vector autoregressions are widely used for macroeconomic forecasting and structural analysis. Until recently, however, most empirical work had considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a variety of shrinkage priors that are useful for tackling the parameter proliferation problem in large Bayesian VARs. This is followed by a detailed discussion of efficient sampling methods for overcoming the computational problem. We then give an overview of some recent models that incorporate various important model features into conventional large Bayesian VARs, including stochastic volatility, non-Gaussian, and serially correlated errors. Efficient estimation methods for fitting these more flexible models are then discussed. These models and methods are illustrated using a forecasting exercise that involves a real-time macroeconomic dataset. The corresponding Matlab code is also provided [Matlab code is available at http://joshuachan.org/]
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