Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap

This paper investigates the use of bootstrap-based bias correction of semi-parametric estimators of the long memory parameter in fractionally integrated processes. The re-sampling method involves the application of the sieve bootstrap to data pre-filtered by a preliminary semi-parametric estimate of the long memory parameter. Theoretical justification for using the bootstrap techniques to bias adjust log-periodogram and semi-parametric local Whittle estimators of the memory parameter is provided. Simulation evidence comparing the performance of the bootstrap bias correction with analytical bias correction techniques is also presented. The bootstrap method is shown to produce notable bias reductions, in particular when applied to an estimator for which analytical adjustments have already been used. The empirical coverage of confidence intervals based on the bias-adjusted estimators is very close to the nominal, for a reasonably large sample size, more so than for the comparable analytically adjusted estimators. The precision of inferences (as measured by interval length) is also greater when the bootstrap is used to bias correct rather than analytical adjustments.Comment: 38 page

Probabilistic Forecasts of Volatility and its Risk Premia

The object of this paper is to produce distributional forecasts of physical volatility and its associated risk premia using a non-Gaussian, non-linear state space approach. Option and spot market information on the unobserved variance process is captured by using dual 'model-free' variance measures to define a bivariate observation equation in the state space model. The premium for diffusive variance risk is defined as linear in the latent variance (in the usual fashion) whilst the premium for jump variance risk is specified as a conditionally deterministic dynamic process, driven by a function of past measurements. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo algorithm that caters for the multiple sources of non-linearity in the model and the bivariate measure. The method is applied to empirical spot and option price data for the S&P500 index over the 1999 to 2008 period, with conclusions drawn about investors' required compensation for variance risk during the recent financial turmoil. The accuracy of the probabilistic forecasts of the observable variance measures is demonstrated, and compared with that of forecasts yielded by more standard time series models. To illustrate the benefits of the approach, the posterior distribution is augmented by information on daily returns to produce Value at Risk predictions, as well as being used to yield forecasts of the prices of derivatives on volatility itself. Linking the variance risk premia to the risk aversion parameter in a representative agent model, probabilistic forecasts of relative risk aversion are also produced.Volatility Forecasting; Non-linear State Space Models; Non-parametric Variance Measures; Bayesian Markov Chain Monte Carlo; VIX Futures; Risk Aversion.

Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes

This paper investigates the accuracy of bootstrap-based inference in the case of long memory fractionally integrated processes. The re-sampling method is based on the semi-parametric sieve approach, whereby the dynamics in the process used to produce the bootstrap draws are captured by an autoregressive approximation. Application of the sieve method to data pre-filtered by a semi-parametric estimate of the long memory parameter is also explored. Higher-order improvements yielded by both forms of re-sampling are demonstrated using Edgeworth expansions for a broad class of statistics that includes first- and second-order moments, the discrete Fourier transform and regression coefficients. The methods are then applied to the problem of estimating the sampling distributions of the sample mean and of selected sample autocorrelation coefficients, in experimental settings. In the case of the sample mean, the pre-filtered version of the bootstrap is shown to avoid the distinct underestimation of the sampling variance of the mean which the raw sieve method demonstrates in finite samples, higher order accuracy of the latter notwithstanding. Pre-filtering also produces gains in terms of the accuracy with which the sampling distributions of the sample autocorrelations are reproduced, most notably in the part of the parameter space in which asymptotic normality does not obtain. Most importantly, the sieve bootstrap is shown to reproduce the (empirically infeasible) Edgeworth expansion of the sampling distribution of the autocorrelation coefficients, in the part of the parameter space in which the expansion is valid

Fibroblast Growth Factor 22 Is Not Essential for Skin Development and Repair but Plays a Role in Tumorigenesis

PMCID: PMC3380851This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Polar ozone

The observation and interpretation of a large, unexpected ozone depletion over Antarctica has changed the international scientific view of stratospheric chemistry. The observations which show the veracity, seasonal nature, and vertical structure of the Antarctic ozone hole are presented. Evidence for Arctic and midlatitude ozone loss is also discussed. The chemical theory for Antarctic ozone depletion centers around the occurrence of polar stratospheric clouds (PSCs) in Antarctic winter and spring; the climatology and radiative properties of these clouds are presented. Lab studies of the physical properties of PSCs and the chemical processes that subsequently influence ozone depletion are discussed. Observations and interpretation of the chemical composition of the Antarctic stratosphere are described. It is shown that the observed, greatly enhanced abundances of chlorine monoxide in the lower stratosphere are sufficient to explain much if not all of the ozone decrease. The dynamic meteorology of both polar regions is given, interannual and interhemispheric variations in dynamical processes are outlined, and their likely roles in ozone loss are discussed

Analysis of observations of the middle atmosphere from satellites

Satellite data are being used to investigate problems in middle atmosphere chemistry and dynamics. Efforts have been focused primarily on studies to determine the quality of observed distributions of trace species and derived dynamical quantities. Those data have been used as diagnostics for model-derived constituent profiles and fields and for improving our understanding of some of the fundamental processes occurring in the middle atmosphere. Temperatures and derived winds from Nimbus 7 Limb Infrared Monitoring of the Stratosphere (LIMS) data were compared with long-time series of rawinsonde data at Invercargill, New Zealand, and Berlin, West Germany, and the results are excellent for both quantities. It was also demonstrated that more highly-derived dynamical quantities can be obtained reliably from those LIMS fields. Furthermore, both the diabatic and residual-mean circulations derived using LIMS data agree qualitatively with changes in the distribution of trace species determined independently with the Nimbus 7 SAMS and LIMS experiments. Subsequently, an examination of LIMS data at mid to high latitudes of the Southern Hemisphere has revealed a synoptic-scale, upper stratospheric instability during late autumn that is associated with the development of the stratospheric polar jet. Investigation of this phenomenon continues with Stratospheric Sounding Unit (SSU) data sets

Applicability constraints of the Equivalence Theorem

In this work we study the applicability of the Equivalence Theorem, either for unitary models or within an effective lagrangian approach. There are two types of limitations: the existence of a validity energy window and the use of the lowest order in the electroweak constants. For the first kind, we consider some methods, based on dispersion theory or the large $N$ limit, that allow us to extend the applicability. For the second, we have obtained numerical estimates of the effect of neglecting higher orders in the perturbative expansion.Comment: Final version to appear in Phys. Rev. D. Power counting and energy range estimates have been refined, improved referencing. 4 postscript figures, uses revtex. FT-UCM 1/9

Incorporating next-to-leading order matrix elements for hadronic diboson production in showering event generators

A method for incorporating information from next-to-leading order QCD matrix elements for hadronic diboson production into showering event generators is presented. In the hard central region (high jet transverse momentum) where perturbative QCD is reliable, events are sampled according to the first order tree level matrix element. In the soft and collinear regions next-to-leading order corrections are approximated by calculating the differential cross section across the phase space accessible to the parton shower using the first order (virtual graphs included) matrix element. The parton shower then provides an all-orders exclusive description of parton emissions. Events generated in this way provide a physical result across the entire jet transverse momentum spectrum, have next-to-leading order normalization everywhere, and have positive definite event weights. The method is generalizable without modification to any color singlet production process.Comment: 13 pages, 9 figure

Complete Genome Sequences of Paenibacillus Larvae Phages BN12, Dragolir, Kiel007, Leyra, Likha, Pagassa, PBL1c, and Tadhana

We present here the complete genomes of eight phages that infect Paenibacillus larvae, the causative agent of American foulbrood in honeybees. Phage PBL1c was originally isolated in 1984 from a P. larvae lysogen, while the remaining phages were isolated in 2014 from bee debris, honeycomb, and lysogens from three states in the USA

Community Influences on Female Genital Mutilation/Cutting in Kenya: Norms, Opportunities, and Ethnic Diversity

Female genital mutilation/cutting (FGMC) is a human rights violation with adverse health consequences. Although prevalence is declining, the practice persists in many countries, and the individual and contextual risk factors associated with FGMC remain poorly understood. We propose an integrated theory about contextual factors and test it using multilevel discrete-time hazard models in a nationally representative sample of 7,535 women with daughters who participated in the 2014 Kenya Demographic and Health Survey. A daughter’s adjusted hazard of FGMC was lower if she had an uncut mother who disfavored FGMC, lived in a community that was more opposed to FGMC, and lived in a more ethnically diverse community. Unexpectedly, a daughter’s adjusted FGMC hazard was higher if she lived in a community with more extrafamilial opportunities for women. Other measures of women’s opportunities warrant consideration, and interventions to shift FGMC norms in more ethnically diverse communities show promise to accelerate abandonment