Análise bayesiana do modelo fatorial dinâmico para um vetor de séries temporais utilizando a distribuição t multivariada

The multivariate t models are symmetric and with heavier tail than the normal distribution, important feature in financial data. In this theses is presented the Bayesian estimation of a dynamic factor model, where the factors follow a multivariate autoregressive model, using multivariate t distribution. Since the multivariate t distribution is complex, it was represented in this work as a mix between a multivariate normal distribution and a square root of a chi-square distribution. This method allowed to define the posteriors. The inference on the parameters was made taking a sample of the posterior distribution, through the Gibbs Sampler. The convergence was verified through graphical analysis and the convergence tests Geweke (1992) and Raftery & Lewis (1992a). The method was applied in simulated data and in the indexes of the major stock exchanges in the world.

Measurement of the elastic electron-proton cross section and separation of the electric and magnetic form factor in the Q 2 range from 0.004 to 1 (GeV/c) 2

The electromagnetic form factors of the proton are fundamental quantities sensitive to the distribution of charge and magnetization inside the proton. Precise knowledge of the form factors, in particular of the charge and magnetization radii provide strong tests for theory in the non-perturbative regime of QCD. However, the existing data at Q^2 below 1 (GeV/c)^2 are not precise enough for a hard test of theoretical predictions.rnrnFor a more precise determination of the form factors, within this work more than 1400 cross sections of the reaction H(e,e′)p were measured at the Mainz Microtron MAMI using the 3-spectrometer-facility of the A1-collaboration. The data were taken in three periods in the years 2006 and 2007 using beam energies of 180, 315, 450, 585, 720 and 855 MeV. They cover the Q^2 region from 0.004 to 1 (GeV/c)^2 with counting rate uncertainties below 0.2% for most of the data points. The relative luminosity of the measurements was determined using one of the spectrometers as a luminosity monitor. The overlapping acceptances of the measurements maximize the internal redundancy of the data and allow, together with several additions to the standard experimental setup, for tight control of systematic uncertainties.rnTo account for the radiative processes, an event generator was developed and implemented in the simulation package of the analysis software which works without peaking approximation by explicitly calculating the Bethe-Heitler and Born Feynman diagrams for each event.rnTo separate the form factors and to determine the radii, the data were analyzed by fitting a wide selection of form factor models directly to the measured cross sections. These fits also determined the absolute normalization of the different data subsets. The validity of this method was tested with extensive simulations. The results were compared to an extraction via the standard Rosenbluth technique.rnrnThe dip structure in G_E that was seen in the analysis of the previous world data shows up in a modified form. When compared to the standard-dipole form factor as a smooth curve, the extracted G_E exhibits a strong change of the slope around 0.1 (GeV/c)^2, and in the magnetic form factor a dip around 0.2 (GeV/c)^2 is found. This may be taken as indications for a pion cloud. For higher Q^2, the fits yield larger values for G_M than previous measurements, in agreement with form factor ratios from recent precise polarized measurements in the Q2 region up to 0.6 (GeV/c)^2.rnrnThe charge and magnetic rms radii are determined as rn⟨r_e⟩=0.879 ± 0.005(stat.) ± 0.004(syst.) ± 0.002(model) ± 0.004(group) fm,rn⟨r_m⟩=0.777 ± 0.013(stat.) ± 0.009(syst.) ± 0.005(model) ± 0.002(group) fm.rnThis charge radius is significantly larger than theoretical predictions and than the radius of the standard dipole. However, it is in agreement with earlier results measured at the Mainz linear accelerator and with determinations from Hydrogen Lamb shift measurements. The extracted magnetic radius is smaller than previous determinations and than the standard-dipole value.

Factor Structure of the Neck Disability Index

BACKGROUND CONTEXT: The Neck Disability Index frequently is used to measure outcomes of the neck. The statistical rigor of the Neck Disability Index has been assessed with conflicting outcomes. To date, Confirmatory Factor Analysis of the Neck Disability Index has not been reported for a suitably large population study. Because the Neck Disability Index is not a condition-specific measure of neck function, initial Confirmatory Factor Analysis should consider problematic neck patients as a homogenous group. PURPOSE: We sought to analyze the factor structure of the Neck Disability Index through Confirmatory Factor Analysis in a symptomatic, homogeneous, neck population, with respect to pooled populations and gender subgroups. STUDY DESIGN: This was a secondary analysis of pooled data. PATIENT SAMPLE: A total of 1,278 symptomatic neck patients (67.5% female, median age 41 years), 803 nonspecific and 475 with whiplash-associated disorder. OUTCOME MEASURES: The Neck Disability Index was used to measure outcomes. METHODS: We analyzed pooled baseline data from six independent studies of patients with neck problems who completed Neck Disability Index questionnaires at baseline. The Confirmatory Factor Analysis was considered in three scenarios: the full sample and separate sexes. Models were compared empirically for best fit. RESULTS: Two-factor models have good psychometric properties across both the pooled and sex subgroups. However, according to these analyses, the one-factor solution is preferable from both a statistical perspective and parsimony. The two-factor model was close to significant for the male subgroup (p<.07) where questions separated into constructs of mental function (pain, reading headaches and concentration) and physical function (personal care, lifting, work, driving, sleep, and recreation). CONCLUSIONS: The Neck Disability Index demonstrated a one-factor structure when analyzed by Confirmatory Factor Analysis in a pooled, homogenous sample of neck problem patients. However, a two-factor model did approach significance for male subjects where questions separated into constructs of mental and physical function. Further investigations in different conditions, subgroup and sex-specific populations are warranted.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

Modelos para el análisis dinámico y la predicción a corto plazo de los precios de la electricidad en mercados liberalizados

En esta tesis se va a describir y aplicar de forma novedosa la técnica del alisado exponencial multivariante a la predicción a corto plazo, a un día vista, de los precios horarios de la electricidad, un problema que se está estudiando intensivamente en la literatura estadística y económica reciente. Se van a demostrar ciertas propiedades interesantes del alisado exponencial multivariante que permiten reducir el número de parámetros para caracterizar la serie temporal y que al mismo tiempo permiten realizar un análisis dinámico factorial de la serie de precios horarios de la electricidad. En particular, este proceso multivariante de elevada dimensión se estimará descomponiéndolo en un número reducido de procesos univariantes independientes de alisado exponencial caracterizado cada uno por un solo parámetro de suavizado que variará entre cero (proceso de ruido blanco) y uno (paseo aleatorio). Para ello, se utilizará la formulación en el espacio de los estados para la estimación del modelo, ya que ello permite conectar esa secuencia de modelos univariantes más eficientes con el modelo multivariante. De manera novedosa, las relaciones entre los dos modelos se obtienen a partir de un simple tratamiento algebraico sin requerir la aplicación del filtro de Kalman. De este modo, se podrán analizar y poner al descubierto las razones últimas de la dinámica de precios de la electricidad. Por otra parte, la vertiente práctica de esta metodología se pondrá de manifiesto con su aplicación práctica a ciertos mercados eléctricos spot, tales como Omel, Powernext y Nord Pool. En los citados mercados se caracterizará la evolución de los precios horarios y se establecerán sus predicciones comparándolas con las de otras técnicas de predicción. ABSTRACT This thesis describes and applies the multivariate exponential smoothing technique to the day-ahead forecast of the hourly prices of electricity in a whole new way. This problem is being studied intensively in recent statistics and economics literature. It will start by demonstrating some interesting properties of the multivariate exponential smoothing that reduce drastically the number of parameters to characterize the time series and that at the same time allow a dynamic factor analysis of the hourly prices of electricity series. In particular this very complex multivariate process of dimension 24 will be estimated by decomposing a very reduced number of univariate independent of exponentially smoothing processes each characterized by a single smoothing parameter that varies between zero (white noise process) and one (random walk). To this end, the formulation is used in the state space model for the estimation, since this connects the sequence of efficient univariate models to the multivariate model. Through a novel way, relations between the two models are obtained from a simple algebraic treatment without applying the Kalman filter. Thus, we will analyze and expose the ultimate reasons for the dynamics of the electricity price. Moreover, the practical aspect of this methodology will be shown by applying this new technique to certain electricity spot markets such as Omel, Powernext and Nord Pool. In those markets the behavior of prices will be characterized, their predictions will be formulated and the results will be compared with those of other forecasting techniques.

Dynamic behaviour of underspanned suspension road bridges under traffic loads

Underspanned suspension bridges are structures with important economical and aesthetic advantages, due to their high structural efficiency. However, road bridges of this typology are still uncommon because of limited knowledge about this structural system. In particular, there remains some uncertainty over the dynamic behaviour of these bridges, due to their extreme lightness. The vibrations produced by vehicles crossing the viaduct are one of the main concerns. In this work, traffic-induced dynamic effects on this kind of viaduct are addressed by means of vehicle-bridge dynamic interaction models. A finite element method is used for the structure, and multibody dynamic models for the vehicles, while interaction is represented by means of the penalty method. Road roughness is included in this model in such a way that the fact that profiles under left and right tyres are different, but not independent, is taken into account. In addition, free software {PRPgenerator) to generate these profiles is presented in this paper. The structural dynamic sensitivity of underspanned suspension bridges was found to be considerable, as well as the dynamic amplification factors and deck accelerations. It was also found that vehicle speed has a relevant influence on the results. In addition, the impact of bridge deformation on vehicle vibration was addressed, and the effect on the comfort of vehicle users was shown to be negligible.

Comparing fiscal multipliers across models and countries in Europe. National Bank of Belgium Working Paper No. 278, March 2015

This paper employs fifteen dynamic macroeconomic models maintained within the European System of Central Banks to assess the size of fiscal multipliers in European countries. Using a set of common simulations, we consider transitory and permanent shocks to government expenditures and different taxes. We investigate how the baseline multipliers change when monetary policy is transitorily constrained by the zero nominal interest rate bound, certain crisis-related structural features of the economy such as the share of liquidity-constrained households change, and the endogenous fiscal rule that ensures fiscal sustainability in the long run is specified in terms of labour income taxes instead of lump-sum taxes.

Essays in macroeconometrics

The knowledge of the current state of the economy is crucial for policy makers, economists and analysts. However, a key economic variable, the gross domestic product (GDP), are typically colected on a quartely basis and released with substancial delays by the national statistical agencies. The first aim of this paper is to use a dynamic factor model to forecast the current russian GDP, using a set of timely monthly information. This approach can cope with the typical data flow problems of non-synchronous releases, mixed frequency and the curse of dimensionality. Given that Russian economy is largely dependent on the commodity market, our second motivation relates to study the effects of innovations in the russian macroeconomic fundamentals on commodity price predictability. We identify these innovations through a news index which summarizes deviations of offical data releases from the expectations generated by the DFM and perform a forecasting exercise comparing the performance of different models.

Single-subject research designs and data analyses for assessing elite athletes' conditioning

Research in conditioning (all the processes of preparation for competition) has used group research designs, where multiple athletes are observed at one or more points in time. However, empirical reports of large inter-individual differences in response to conditioning regimens suggest that applied conditioning research would greatly benefit from single-subject research designs. Single-subject research designs allow us to find out the extent to which a specific conditioning regimen works for a specific athlete, as opposed to the average athlete, who is the focal point of group research designs. The aim of the following review is to outline the strategies and procedures of single-subject research as they pertain to.. the assessment of conditioning for individual athletes. The four main experimental designs in single-subject research are: the AB design, reversal (withdrawal) designs and their extensions, multiple baseline designs and alternating treatment designs. Visual and statistical analyses commonly used to analyse single-subject data, and advantages and limitations are discussed. Modelling of multivariate single-subject data using techniques such as dynamic factor analysis and structural equation modelling may identify individualised models of conditioning leading to better prediction of performance. Despite problems associated with data analyses in single-subject research (e.g. serial dependency), sports scientists should use single-subject research designs in applied conditioning research to understand how well an intervention (e.g. a training method) works and to predict performance for a particular athlete.

Depression in young adolescents: Investigations using 2 and 3 factor versions of the parental bonding instrument

Associations between parenting style and depressive symptomatology in a community sample of young adolescents (N = 2596) were investigated using self-report measures including the Parental Bonding Instrument and the Center for Epidemiologic Studies Depression Scale. Specifically, the 25-item 2-factor and 3-factor models by Parker et al. (1979), Kendler's (1996) 16-item 3-factor model, and Parker's (1983) quadrant model for the Parental Bonding Instrument were compared. Data analysis included analysis of variance and logistic regression. Reanalysis of Parker's original scale indicates that overprotection is composed of separate factors: intrusiveness (at the individual level) and restrictiveness (in the social context). All models reveal significant independent contributions from paternal care, maternal care, and maternal overprotection (2-factor) or intrusiveness (3-factor) to moderate and serious depressive symptomatology, controlling for sex and family living arrangement. Additive rather than multiplicative interactions between care and overprotection were found. Regardless of the level of parental care and affection, clinicians should note that maternal intrusiveness is strongly associated with adverse psychosocial health in young adolescents.

Factor structure of the Mood and Anxiety Symptom Questionnaire does not generalize to an anxious/depressed sample

Objective: The tripartite model of anxiety and depression has been proposed as a representation of the structure of anxiety and depression symptoms. The Mood and Anxiety Symptom Questionnaire (MASQ) has been put forwards as a valid measure of the tripartite model of anxiety and depression symptoms. This research set out to examine the factor structure of anxiety and depression symptoms in a clinical sample to assess the MASQ's validity for use in this population. MethodsThe present study uses confirmatory factor analytic methods to examine the psychometric properties of the MASQ in 470 outpatients with anxiety and mood disorder. Results: The results showed that none of the previously reported two-factor, three-factor or five-factor models adequately fit the data, irrespective of whether items or subscales were used as the unit of analysis. Conclusions: It was concluded that the factor structure of the MASQ in a mixed anxiety/depression clinical sample does not support a structure consistent with the tripartite model. This suggests that researchers using the MASQ with anxious/depressed individuals should be mindful of the instrument's psychometric limitations.

A study of the dynamic behaviour of a pulsed column solvent extraction plant

A study has been made of the dynamic behaviour of a nuclear fuel reprocessing plant utilising pulsed solvent extraction columns. A flowsheet is presented and the choice of an extraction device is discussed. The plant is described by a series of modules each module representing an item of equipment. Each module consists of a series of differential equations describing the dynamic behaviour of the equipment. The model is written in PMSP, a language developed for dynamic simulation models. The differential equations are solved to predict plant behaviour with time. The dynamic response of the plant to a range of disturbances has been assessed. The interactions between pulsed columns have been demonstrated and illustrated. The importance of auxillary items of equipment to plant performance is demonstrated. Control of the reprocessing plant is considered and the effect of control parameters on performance assessed.

Wealth effects of mergers and acquisitions for US firms: using alternative pricing models

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

Integrating OpenMI and Uncertweb:managing uncertainty in OpenMI models

OpenMI is a widely used standard allowing exchange of data between integrated models, which has mostly been applied to dynamic, deterministic models. Within the FP7 UncertWeb project we are developing mechanisms and tools to support the management of uncertainty in environmental models. In this paper we explore the integration of the UncertWeb framework with OpenMI, to assess the issues that arise when propagating uncertainty in OpenMI model compositions, and the degree of integration possible with UncertWeb tools. In particular we develop an uncertainty-enabled model for a simple Lotka-Volterra system with an interface conforming to the OpenMI standard, exploring uncertainty in the initial predator and prey levels, and the parameters of the model equations. We use the Elicitator tool developed within UncertWeb to identify the initial condition uncertainties, and show how these can be integrated, using UncertML, with simple Monte Carlo propagation mechanisms. The mediators we develop for OpenMI models are generic and produce standard Web services that expose the OpenMI models to a Web based framework. We discuss what further work is needed to allow a more complete system to be developed and show how this might be used practically.