Two different 3D biogeochemical models for the NW mediterranean sea: validation with Meris data and intercomparison

Report for the scientific sojourn carried out at the University of New South Wales from February to June the 2007. Two different biogeochemical models are coupled to a three dimensional configuration of the Princeton Ocean Model (POM) for the Northwestern Mediterranean Sea (Ahumada and Cruzado, 2007). The first biogeochemical model (BLANES) is the three-dimensional version of the model described by Bahamon and Cruzado (2003) and computes the nitrogen fluxes through six compartments using semi-empirical descriptions of biological processes. The second biogeochemical model (BIOMEC) is the biomechanical NPZD model described in Baird et al. (2004), which uses a combination of physiological and physical descriptions to quantify the rates of planktonic interactions. Physical descriptions include, for example, the diffusion of nutrients to phytoplankton cells and the encounter rate of predators and prey. The link between physical and biogeochemical processes in both models is expressed by the advection-diffusion of the non-conservative tracers. The similarities in the mathematical formulation of the biogeochemical processes in the two models are exploited to determine the parameter set for the biomechanical model that best fits the parameter set used in the first model. Three years of integration have been carried out for each model to reach the so called perpetual year run for biogeochemical conditions. Outputs from both models are averaged monthly and then compared to remote sensing images obtained from sensor MERIS for chlorophyll.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

Lack of evidence of the interaction of the Aß peptide with the Wnt signaling cascade in Drosophila models of Alzheimer's disease.

Background Alzheimer's disease (AD) is the leading form of dementia worldwide. The Aß-peptide is believed to be the major pathogenic compound of the disease. Since several years it is hypothesized that Aß impacts the Wnt signaling cascade and therefore activation of this signaling pathway is proposed to rescue the neurotoxic effect of Aß. Findings Expression of the human Aß42 in the Drosophila nervous system leads to a drastically shortened life span. We found that the action of Aß42 specifically in the glutamatergic motoneurons is responsible for the reduced survival. However, we find that the morphology of the glutamatergic larval neuromuscular junctions, which are widely used as the model for mammalian central nervous system synapses, is not affected by Aß42 expression. We furthermore demonstrate that genetic activation of the Wnt signal transduction pathway in the nervous system is not able to rescue the shortened life span or a rough eye phenotype in Drosophila. Conclusions Our data confirm that the life span is a useful readout of Aß42 induced neurotoxicity in Drosophila; the neuromuscular junction seems however not to be an appropriate model to study AD in flies. Additionally, our results challenge the hypothesis that Wnt signaling might be implicated in Aß42 toxicity and might serve as a drug target against AD.

Revisiting the Dollar-Euro Permanent Equilibrium Exchange Rate: Evidence from Multivariate Unobserved Components Models

We propose an alternative approach to obtaining a permanent equilibrium exchange rate (PEER), based on an unobserved components (UC) model. This approach offers a number of advantages over the conventional cointegration-based PEER. Firstly, we do not rely on the prerequisite that cointegration has to be found between the real exchange rate and macroeconomic fundamentals to obtain non-spurious long-run relationships and the PEER. Secondly, the impact that the permanent and transitory components of the macroeconomic fundamentals have on the real exchange rate can be modelled separately in the UC model. This is important for variables where the long and short-run effects may drive the real exchange rate in opposite directions, such as the relative government expenditure ratio. We also demonstrate that our proposed exchange rate models have good out-of sample forecasting properties. Our approach would be a useful technique for central banks to estimate the equilibrium exchange rate and to forecast the long-run movements of the exchange rate.

The Role of Institutions in Cross-Section Income and Panel Data Growth Models: A Deeper Investigation on the Weakness and Proliferation of Instruments

This paper investigates the role of institutions in determining per capita income levels and growth. It contributes to the empirical literature by using different variables as proxies for institutions and by developing a deeper analysis of the issues arising from the use of weak and too many instruments in per capita income and growth regressions. The cross-section estimation suggests that institutions seem to matter, regardless if they are the only explanatory variable or are combined with geographical and integration variables, although most models suffer from the issue of weak instruments. The results from the growth models provides some interesting results: there is mixed evidence on the role of institutions and such evidence is more likely to be associated with law and order and investment profile; government spending is an important policy variable; collapsing the number of instruments results in fewer significant coefficients for institutions.

The Change Questionnaire predicts change in hazardous tobacco and alcohol use.

INTRODUCTION: Assessing motivation for change is deemed an important step in the treatment process that allows further refinement of the intervention in motivational interviewing (MI) and brief MI (BMI) adaptations. During MI (and BMI) sessions, motivation for change is expressed by the client as "change talk", i.e. all statements inclined toward or away from change. We tested the predictive validity of the Change Questionnaire, a 12-item instrument assessing motivation to change, on hazardous tobacco and alcohol use. METHODS: As part of the baseline measurements for a randomized controlled trial on multi-substance BMI at the Lausanne recruitment center (army conscription is mandatory in Switzerland for males at age 20, and thus provides a unique opportunity to address a non-clinical and largely representative sample of young men), 213 participants completed the questionnaire on tobacco and 95 on alcohol and were followed-up six months later. The overall Change Questionnaire score and its six subscales (Desire, Ability, Reasons, Need, Commitment, and Taking steps) were used as predictors of hazardous tobacco use (defined as daily smoking) and hazardous alcohol use (defined as more than one occasion with six standard drinks or more per month, and/or more than 21 standard drinks per week) in bivariate logistic regression models at follow-up. RESULTS: Higher overall Change scores were significant predictors of decreased risk for hazardous tobacco (odds ratio [OR] = 0.83, p = 0.046) and alcohol (OR = 0.76, p = 0.03) use. Several sub-dimensions were associated with the outcomes in bivariate analyses. Using a principal components analysis to reduce the number of predictors for multivariate models, we obtained two components. 'Ability to change' was strongly related to change in hazardous tobacco use (OR = 0.54, p < 0.001), the second we interpreted as 'Other change language dimensions' and which was significantly related to change in hazardous alcohol use (OR = 0.81, p = 0.05). CONCLUSIONS: The present findings lend initial support for the predictive validity of the Change Questionnaire on hazardous tobacco and alcohol use, making it an interesting and potentially useful tool for assessing motivation to change among young males.

Evaluation of tumour vascularisation in two rat sarcoma models for studying isolated lung perfusion. Injection route determines the origin of tumour vessels.

Isolated cytostatic lung perfusion (ILP) is an attractive technique allowing delivery of a high-dose of cytostatic agents to the lungs while limiting systemic toxicity. In developing a rat model of ILP, we have analysed the effect of the route of tumour cell injection on the source of tumour vessels. Pulmonary sarcomas were established by injecting a sarcoma cell suspension either by the intravenous (i.v.) route or directly into the lung parenchyma. Ink perfusion through either pulmonary artery (PA) or bronchial arteries (BA) was performed and the characteristics of the tumour deposits defined. i.v. and direct injection methods induced pulmonary sarcoma nodules, with similar histological features. The intraparenchymal injection of tumour cells resulted in more reliable and reproducible tumour growth and was associated with a longer survival of the animals. i.v. injected tumours developed a PA-derived vascular tree whereas directly injected tumours developed a BA-derived vasculature.

Models de simulació per coordinar serveis d'emergència

El projecte exposat té com a propòsit definir i implementar un model de simulació basat en la coordinació i assignació dels serveis d’emergència en accidents de trànsit. La definició del model s’ha realitzat amb l’ús de les Xarxes de Petri Acolorides i la implementació amb el software Rockwell Arena 7.0. El modelatge de la primera simulació ens mostra un model teòric basat en cues mentre que el segon, mostra un model més complet i real gràcies a la connexió mitjançant la plataforma Corba a una base de dades amb informació geogràfica de les flotes i de les rutes. Com a resultat de l’estudi i amb l’ajuda de GoogleEarth, podem realitzar simulacions gràfiques per veure els accidents generats, les flotes dels serveis i el moviment dels vehicles des de les bases fins als accidents.

On Identification of Bayesian DSGE Models

In recent years there has been increasing concern about the identification of parameters in dynamic stochastic general equilibrium (DSGE) models. Given the structure of DSGE models it may be difficult to determine whether a parameter is identified. For the researcher using Bayesian methods, a lack of identification may not be evident since the posterior of a parameter of interest may differ from its prior even if the parameter is unidentified. We show that this can even be the case even if the priors assumed on the structural parameters are independent. We suggest two Bayesian identification indicators that do not suffer from this difficulty and are relatively easy to compute. The first applies to DSGE models where the parameters can be partitioned into those that are known to be identified and the rest where it is not known whether they are identified. In such cases the marginal posterior of an unidentified parameter will equal the posterior expectation of the prior for that parameter conditional on the identified parameters. The second indicator is more generally applicable and considers the rate at which the posterior precision gets updated as the sample size (T) is increased. For identified parameters the posterior precision rises with T, whilst for an unidentified parameter its posterior precision may be updated but its rate of update will be slower than T. This result assumes that the identified parameters are pT-consistent, but similar differential rates of updates for identified and unidentified parameters can be established in the case of super consistent estimators. These results are illustrated by means of simple DSGE models.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.