Characterizing economic trends by Bayesian stochastic model specification search

We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends. In particular, we formulate autoregressive models with stochastic trends components and decide on whether a specific feature of the series, i.e. the underlying level and/or the rate of drift, are fixed or evolutive.Bayesian model selection; stationarity; unit roots; stochastic trends; variable selection.

Bayesian stochastic model specification search for seasonal and calendar effects

We apply a recent methodology, Bayesian stochastic model specification search (SMSS), for the selection of the unobserved components (level, slope, seasonal cycles, trading days effects) that are stochastically evolving over time. SMSS hinges on two basic ingredients: the non-centered representation of the unobserved components and the reparameterization of the hyperparameters representing standard deviations as regression parameters with unrestricted support. The choice of the prior and the conditional independence structure of the model enable the definition of a very efficient MCMC estimation strategy based on Gibbs sampling. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends, fixed and evolutive seasonal and trading day effects.Seasonality; Structural time series models; Variable selection.

How to measure Corporate Social Responsibility

Compliance with Corporate Social Responsibility (CSR) standards may require capacity that varies from one aspect to the other and companies in different industries may encounter different difficulties. Since CSR is a multidimensional concept, latent variable models may be usefully employed to provide a unidimensional measure of the ability of a firm to fulfil CSR standards. A methodology based on Item Response Theory has been implemented on the KLD sustainability dataset. Results show that companies in the industries Oil & Gas, Industrials, Basic Materials and Telecommunications have a higher difficulty to meet the CSR standards. Criteria based on Environment, Community relations and Product quality have a large capacity to select the firms with the best CSR performance, while Governance does not exhibit similar behavior. A stock selection based on the ranking of the firms according to our CSR measure outperforms, in terms of risk-adjusted returns, stock selection based on other criteria.Socially Responsible Investment, CSR ability, latent variable model, item response theory

H2_2 ortho-to-para conversion on grains: A route to fast deuterium fractionation in dense cloud cores?

Deuterium fractionation, i.e. the enhancement of deuterated species with respect to the non-deuterated ones, is considered to be a reliable chemical clock of star-forming regions. This process is strongly affected by the ortho-to-para (o-p) H$_2$ ratio. In this letter we explore the effect of the o-p H$_2$ conversion on grains on the deuteration timescale in fully depleted dense cores, including the most relevant uncertainties that affect this complex process. We show that (i) the o-p H$_2$ conversion on grains is not strongly influenced by the uncertainties on the conversion time and the sticking coefficient and (ii) that the process is controlled by the temperature and the residence time of ortho-H$_2$ on the surface, i.e. by the binding energy. We find that for binding energies in between 330-550 K, depending on the temperature, the o-p H$_2$ conversion on grains can shorten the deuterium fractionation timescale by orders of magnitude, opening a new route to explain the large observed deuteration fraction $D_\mathrm{frac}$ in dense molecular cloud cores. Our results suggest that the star formation timescale, when estimated through the timescale to reach the observed deuteration fractions, might be shorter than previously proposed. However, more accurate measurements of the binding energy are needed to better assess the overall role of this process.Comment: Accepted for publication in ApJ Letter

Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search

An important issue in modelling economic time series is whether key unobserved components representing trends, seasonality and calendar components, are deterministic or evolutive. We address it by applying a recently proposed Bayesian variable selection methodology to an encompassing linear mixed model that features, along with deterministic effects, additional random explanatory variables that account for the evolution of the underlying level, slope, seasonality and trading days. Variable selection is performed by estimating the posterior model probabilities using a suitable Gibbs sampling scheme. The paper conducts an extensive empirical application on a large and representative set of monthly time series concerning industrial production and retail turnover. We find strong support for the presence of stochastic trends in the series, either in the form of a time-varying level, or, less frequently, of a stochastic slope, or both. Seasonality is a more stable component: only in 70% of the cases we were able to select at least one stochastic trigonometric cycle out of the six possible cycles. Most frequently the time variation is found in correspondence with the fundamental and the first harmonic cycles. An interesting and intuitively plausible finding is that the probability of estimating time-varying components increases with the sample size available. However, even for very large sample sizes we were unable to find stochastically varying calendar effects

The chemical evolution of self-gravitating primordial disks

Numerical simulations show the formation of self-gravitating primordial disks during the assembly of the first structures in the Universe, in particular during the formation of Pop.~III and supermassive stars. Their subsequent evolution is expected to be crucial to determine the mass scale of the first cosmological objects, which depends on the temperature of the gas and the dominant cooling mechanism. Here, we derive a one-zone framework to explore the chemical evolution of such disks and show that viscous heating leads to the collisional dissociation of an initially molecular gas. The effect is relevant on scales of 10 AU (1000 AU) for a central mass of 10 M_sun (10^4 M_sun) at an accretion rate of 10^{-1} M_sun yr^{-1}, and provides a substantial heat input to stabilize the disk. If the gas is initially atomic, it remains atomic during the further evolution, and the effect of viscous heating is less significant. The additional thermal support is particularly relevant for the formation of very massive objects, such as the progenitors of the first supermassive black holes. The stabilizing impact of viscous heating thus alleviates the need for a strong radiation background as a means of keeping the gas atomic.Comment: 13 pages, 5 figures, 6 tables, accepted at A&

A chemical model for the interstellar medium in galaxies

We present and test chemical models for three-dimensional hydrodynamical simulations of galaxies. We explore the effect of changing key parameters such as metallicity, radiation and non-equilibrium versus equilibrium metal cooling approximations on the transition between the gas phases in the interstellar medium. The microphysics is modelled by employing the public chemistry package KROME and the chemical networks have been tested to work in a wide range of densities and temperatures. We describe a simple H/He network following the formation of H$_2$, and a more sophisticated network which includes metals. Photochemistry, thermal processes, and different prescriptions for the H$_2$ catalysis on dust are presented and tested within a one-zone framework. The resulting network is made publicly available on the KROME webpage. We find that employing an accurate treatment of the dust-related processes induces a faster HI--H$_2$ transition. In addition, we show when the equilibrium assumption for metal cooling holds, and how a non-equilibrium approach affects the thermal evolution of the gas and the HII--HI transition. These models can be employed in any hydrodynamical code via an interface to KROME and can be applied to different problems including isolated galaxies, cosmological simulations of galaxy formation and evolution, supernova explosions in molecular clouds, and the modelling of star-forming regions. The metal network can be used for a comparison with observational data of CII 158 $\mu$m emission both for high-redshift as well as for local galaxies.Comment: A&A accepte

A datacleaning augmented Kalman filter for robust estimation of state space models

This article presents a robust augmented Kalman filter that extends the data cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their onestepahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an Mtype estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the com- parative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series