Missing observations in observation-driven time series models

We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean. A Monte Carlo simulation study supports this theoretical result and illustrates how the inconsistency problem extends to score-driven and, more generally, to observation-driven models, which include well-known models for conditional volatility. To overcome the problem of inconsistent inference, we propose a novel estimation procedure based on indirect inference. This easy-to-implement method delivers consistent inference. The asymptotic properties of the new method are formally derived. Our proposed estimation procedure shows a promising performance in a Monte Carlo simulation exercise as well as in an empirical study concerning the measurement of conditional volatility from financial returns data

Realized wishart-garch:A score-driven multi-Asset volatility model

We propose a novel multivariate GARCH model that incorporates realized measures for the covariance matrix of returns. The joint formulation of a multivariate dynamic model for outer-products of returns, realized variances, and realized covariances leads to a feasible approach for analysis and forecasting. The updating of the covariance matrix relies on the score function of the joint likelihood function based on Gaussian and Wishart densities. The dynamic model is parsimonious while the analysis relies on straightforward computations. In a Monte Carlo study, we show that parameters are estimated accurately for different small sample sizes. We illustrate the model with an empirical in-sample and out-of-sample analysis for a portfolio of 15 U.S. financial assets

Cosmic rays studied with a hybrid high school detector array

The LORUN/NAHSA system is a pathfinder for hybrid cosmic ray research combined with education and outreach in the field of astro-particle physics. Particle detectors and radio antennae were mainly setup by students and placed on public buildings. After fully digital data acquisition, coincidence detections were selected. Three candidate events confirmed a working prototype, which can be multiplied to extend further particle detector arrays on high schools.Comment: 10 pages, 6 figures. Nigl, A., Timmermans, C., Schellart, P., Kuijpers, J., Falcke, H., Horneffer, A., de Vos, C. M., Koopman, Y., Pepping, H. J., Schoonderbeek, G., Cosmic rays studied with a hybrid high school detector array, Europhysics News (EPN), Vol. 38, No. 5, accepted on 22/08/200

Retinoic acid, meiosis and germ cell fate in mammals

Although mammalian sex is determined genetically, the sex-specific development of germ cells as sperm or oocytes is initiated by cues provided by the gonadal environment. During embryogenesis, germ cells in an ovary enter meiosis, thereby committing to oogenesis. By contrast, germ cells in a testicular environment do not enter meiosis until puberty. Recent findings indicate that the key to this sex- specific timing of meiosis entry is the presence or absence of the signaling molecule retinoic acid. Although this knowledge clarifies a long- standing mystery in reproductive biology, it also poses many new questions, which we discuss in this review

The Dipole Moments and Molar Refractions of Several Trans-Beta-Nitrostyrenes

The dipole moments and molar refractions are reported for p-nitrostyrene (4.24 D, 44.3 ml.), trans-betanitrostyrene (4.50 D, 45.7 ml.), the p-methoxy (5.45 D, 56.3 ml.), p-methyl (4.97 D, 52.0 ml.), p-fluoro (3-12 D, 45.5ml), p-chloro (2.90 D, 51.8 ml.), p-bromo (3.02 D, 54.4 ml.), p-iodo (3.26 D, 58.0 ml.), p-nitro (0.83 D, 52.0 ml.), and p-cyano 0.96 D, 47.9 ml.) derivatives of trans-beta-nitrostyrene. It is suggested that the large dipole moments obtained for the p-nitro and p-cyano-beta-nitrostyrenes may be due to unusually large atomic polarizations which would not be taken into consideration by the present method of measurement and calculation

Universal neural field computation

Turing machines and G\"odel numbers are important pillars of the theory of computation. Thus, any computational architecture needs to show how it could relate to Turing machines and how stable implementations of Turing computation are possible. In this chapter, we implement universal Turing computation in a neural field environment. To this end, we employ the canonical symbologram representation of a Turing machine obtained from a G\"odel encoding of its symbolic repertoire and generalized shifts. The resulting nonlinear dynamical automaton (NDA) is a piecewise affine-linear map acting on the unit square that is partitioned into rectangular domains. Instead of looking at point dynamics in phase space, we then consider functional dynamics of probability distributions functions (p.d.f.s) over phase space. This is generally described by a Frobenius-Perron integral transformation that can be regarded as a neural field equation over the unit square as feature space of a dynamic field theory (DFT). Solving the Frobenius-Perron equation yields that uniform p.d.f.s with rectangular support are mapped onto uniform p.d.f.s with rectangular support, again. We call the resulting representation \emph{dynamic field automaton}.Comment: 21 pages; 6 figures. arXiv admin note: text overlap with arXiv:1204.546

Mechanical similarity as a generalization of scale symmetry

In this paper we study the symmetry known as mechanical similarity (LMS) and present for any monomial potential. We analyze it in the framework of the Koopman-von Neumann formulation of classical mechanics and prove that in this framework the LMS can be given a canonical implementation. We also show that the LMS is a generalization of the scale symmetry which is present only for the inverse square potential. Finally we study the main obstructions which one encounters in implementing the LMS at the quantum mechanical level.Comment: 9 pages, Latex, a new section adde