A partially collapsed Gibbs sampler for Bayesian quantile regression

We introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler. Although the Metropolis-Hastings algorithm has been employed in Bayesian quantile regression, including median regression, PCG has superior convergence properties to an ordinary Gibbs sampler. Moreover, Our PCG sampler algorithm, which is based on a theoretic derivation of an asymmetric Laplace as scale mixtures of normal distributions, requires less computation than the ordinary Gibbs sampler and can significantly reduce the computation involved in approximating the Bayes Factor and marginal likelihood. Like the ordinary Gibbs sampler, the PCG sample can also be used to calculate any associated marginal and predictive distributions. The quantile regression PCG sampler is illustrated by analysing simulated data and the data of length of stay in hospital. The latter provides new insight into hospital perfor-mance. C-code along with an R interface for our algorithms is publicly available on request from the first author. JEL classification: C11, C14, C21, C31, C52, C53

Point interactions in acoustics: one dimensional models

A one dimensional system made up of a compressible fluid and several mechanical oscillators, coupled to the acoustic field in the fluid, is analyzed for different settings of the oscillators array. The dynamical models are formulated in terms of singular perturbations of the decoupled dynamics of the acoustic field and the mechanical oscillators. Detailed spectral properties of the generators of the dynamics are given for each model we consider. In the case of a periodic array of mechanical oscillators it is shown that the energy spectrum presents a band structure.Comment: revised version, 30 pages, 2 figure

An assessment of PenSim2

The Department for Work and Pensions (DWP)’s Pensim2 model is a dynamic microsimulation model. The principal purpose of this model is to estimate the future distribution of pensioner incomes, thus enabling analysis of the distributional effects of proposed changes to pension policy. This paper presents the results of an assessment of Pensim2 by researchers at the IFS. We start by looking at the overall structure of the model, and how it compares with other dynamic policy analysis models across the world. We make recommendations at this stage as to how the overall modelling strategy could be improved. We then go on to analyse the characteristics of most of the individual modules which make up Pensim2, examining the data used and the regression and predictions used in each step. The results from this examination are used to formulate a set of short and medium-term recommendations for developing and improving the model. Finally, we look at what might become possible for the model over a much longer time frame – looking towards developing a ‘Pensim3’ model over the next decade or so

Selection of the Argentine indicator region

Determined from available Argentine crop statistics, selection of the Indicator Region was based on the highest wheat, corn, and soybean producing provinces, which were: Buenos Aires, Cordoba, Entre Rios, and Santa Fe. Each province in Argentina was examined for the availability of LANDSAT data; area, yield and production statistics; crop calendars; and other ancillary data. The Argentine Indicator Region is described

Common Binder Head and Knotter Head Troubles

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Design for waste-management system

Study was made and system defined for water-recovery and solid-waste processing for low-rise apartment complexes. System can be modified to conform with unique requirements of community, including hydrology, geology, and climate. Reclamation is accomplished by treatment process that features reverse-osmosis membranes