Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.

Evaluating balance, stability and gait symmetry of stroke patients using kinematic and dynamic gait lab data

Dissertação de mestrado integrado em Engenharia Biomédica

Estimation of dynamic latent variable models using simulated nonparametric moments

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

Estimation of dynamic latent variable models using simulated nonparametric moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

Modelling world investment markets using threshold conditional correlation models

In this paper we propose a parsimonious regime-switching approach to model the correlations between assets, the threshold conditional correlation (TCC) model. This method allows the dynamics of the correlations to change from one state (or regime) to another as a function of observable transition variables. Our model is similar in spirit to Silvennoinen and Teräsvirta (2009) and Pelletier (2006) but with the appealing feature that it does not suffer from the course of dimensionality. In particular, estimation of the parameters of the TCC involves a simple grid search procedure. In addition, it is easy to guarantee a positive definite correlation matrix because the TCC estimator is given by the sample correlation matrix, which is positive definite by construction. The methodology is illustrated by evaluating the behaviour of international equities, govenrment bonds and major exchange rates, first separately and then jointly. We also test and allow for different parts in the correlation matrix to be governed by different transition variables. For this, we estimate a multi-threshold TCC specification. Further, we evaluate the economic performance of the TCC model against a constant conditional correlation (CCC) estimator using a Diebold-Mariano type test. We conclude that threshold correlation modelling gives rise to a significant reduction in portfolio´s variance.

Three essays in dynamic corporate finance

Summary Throughout my thesis, I elaborate on how real and financing frictions affect corporate decision making under uncertainty, and I explore how firms time their investment and financing decisions given such frictions. While the macroeconomics literature has focused on the impact of real frictions on investment decisions assuming all equity financed firms, the financial economics literature has mainly focused on the study of financing frictions. My thesis therefore assesses the join interaction of real and financing frictions in firms' dynamic investment and financing decisions. My work provides a rationale for the documented poor empirical performance of neoclassical investment models based on the joint effect of real and financing frictions on investment. A major observation relies in how the infrequency of corporate decisions may affect standard empirical tests. My thesis suggests that the book to market sorts commonly used in the empirical asset pricing literature have economic content, as they control for the lumpiness in firms' optimal investment policies. My work also elaborates on the effects of asymmetric information and strategic interaction on firms' investment and financing decisions. I study how firms time their decision to raise public equity when outside investors lack information about their future investment prospects. I derive areal-options model that predicts either cold or hot markets for new stock issues conditional on adverse selection, and I provide a rational approach to study jointly the market timing of corporate decisions and announcement effects in stock returns. My doctoral dissertation therefore contributes to our understanding of how under real and financing frictions may bias standard empirical tests, elaborates on how adverse selection may induce hot and cold markets in new issues' markets, and suggests how the underlying economic behaviour of firms may induce alternative patterns in stock prices.

Dynamic properties of Lennard Jones fluids and liquid metals

The dependence of the dynamic properties of liquid metals and Lennard-Jones fluids on the characteristics of the interaction potentials is analyzed. Molecular-dynamics simulations of liquids in analogous conditions but assuming that their particles interact either through a Lennard-Jones or a liquid-metal potential were carried out. The Lennard-Jones potentials were chosen so that both the effective size of the particles and the depth of the potential well were very close to those of the liquid-metal potentials. In order to investigate the extent to which the dynamic properties of liquids depend on the short-range attractive interactions as well as on the softness of the potential cores, molecular-dynamics simulations of the same systems but assuming purely repulsive interactions with the same potential cores were also performed. The study includes both singleparticle dynamic properties, such as the velocity autocorrelation functions, and collective dynamic properties, such as the intermediate scattering funcfunctions, and collective dynamic properties, such as the intermediate scattering functions, the dynamic structure factors, the longitudinal and transverse current correlations, and the transport coefficients.

Conditional predictive density evaluation in the presence of instabilities

We propose new methods for evaluating predictive densities. The methods includeKolmogorov-Smirnov and Cram?r-von Mises-type tests for the correct specification ofpredictive densities robust to dynamic mis-specification. The novelty is that the testscan detect mis-specification in the predictive densities even if it appears only overa fraction of the sample, due to the presence of instabilities. Our results indicatethat our tests are well sized and have good power in detecting mis-specification inpredictive densities, even when it is time-varying. An application to density forecastsof the Survey of Professional Forecasters demonstrates the usefulness of the proposedmethodologies.

Alternative tests for correct specification of conditional predictive densities

We propose new methods for evaluating predictive densities that focus on the models' actual predictive ability in finite samples. The tests offer a simple way of evaluatingthe correct specification of predictive densities, either parametric or non-parametric.The results indicate that our tests are well sized and have good power in detecting mis-specification in predictive densities. An empirical application to the Survey ofProfessional Forecasters and a baseline Dynamic Stochastic General Equilibrium modelshows the usefulness of our methodology.

Dynamic analysis of belt-drives using the absolute nodal coordinate formulation

Belt-drive systems have been and still are the most commonly used power transmission form in various applications of different scale and use. The peculiar features of the dynamics of the belt-drives include highly nonlinear deformation,large rigid body motion, a dynamical contact through a dry friction interface between the belt and pulleys with sticking and slipping zones, cyclic tension of the belt during the operation and creeping of the belt against the pulleys. The life of the belt-drive is critically related on these features, and therefore, amodel which can be used to study the correlations between the initial values and the responses of the belt-drives is a valuable source of information for the development process of the belt-drives. Traditionally, the finite element models of the belt-drives consist of a large number of elements thatmay lead to computational inefficiency. In this research, the beneficial features of the absolute nodal coordinate formulation are utilized in the modeling of the belt-drives in order to fulfill the following requirements for the successful and efficient analysis of the belt-drive systems: the exact modeling of the rigid body inertia during an arbitrary rigid body motion, the consideration of theeffect of the shear deformation, the exact description of the highly nonlinear deformations and a simple and realistic description of the contact. The use of distributed contact forces and high order beam and plate elements based on the absolute nodal coordinate formulation are applied to the modeling of the belt-drives in two- and three-dimensional cases. According to the numerical results, a realistic behavior of the belt-drives can be obtained with a significantly smaller number of elements and degrees of freedom in comparison to the previously published finite element models of belt-drives. The results of theexamples demonstrate the functionality and suitability of the absolute nodal coordinate formulation for the computationally efficient and realistic modeling ofbelt-drives. This study also introduces an approach to avoid the problems related to the use of the continuum mechanics approach in the definition of elastic forces on the absolute nodal coordinate formulation. This approach is applied to a new computationally efficient two-dimensional shear deformable beam element based on the absolute nodal coordinate formulation. The proposed beam element uses a linear displacement field neglecting higher-order terms and a reduced number of nodal coordinates, which leads to fewer degrees of freedom in a finite element.

Differentiation of Metabolically Distinct Areas within Head and Neck Region using Dynamic 18F-FDG Positron Emission Tomography Imaging

Positron Emission Tomography (PET) using ¹⁸F-FDG is playing a vital role in the diagnosis and treatment planning of cancer. However, the most widely used radiotracer, ¹⁸F-FDG, is not specific for tumours and can also accumulate in inflammatory lesions as well as normal physiologically active tissues making diagnosis and treatment planning complicated for the physicians. Malignant, inflammatory and normal tissues are known to have different pathways for glucose metabolism which could possibly be evident from different characteristics of the time activity curves from a dynamic PET acquisition protocol. Therefore, we aimed to develop new image analysis methods, for PET scans of the head and neck region, which could differentiate between inflammation, tumour and normal tissues using this functional information within these radiotracer uptake areas. We developed different dynamic features from the time activity curves of voxels in these areas and compared them with the widely used static parameter, SUV, using Gaussian Mixture Model algorithm as well as K-means algorithm in order to assess their effectiveness in discriminating metabolically different areas. Moreover, we also correlated dynamic features with other clinical metrics obtained independently of PET imaging. The results show that some of the developed features can prove to be useful in differentiating tumour tissues from inflammatory regions and some dynamic features also provide positive correlations with clinical metrics. If these proposed methods are further explored then they can prove to be useful in reducing false positive tumour detections and developing real world applications for tumour diagnosis and contouring.

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.

Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We establish the asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.d.s. errors subject to possible conditional heteroskedasticity of unknown form. These proposals are the fixed-design wild bootstrap, the recursive-design wild bootstrap and the pairwise bootstrap. In a simulation study all three procedures tend to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors. In contrast, standard residual-based bootstrap methods for models with i.i.d. errors may be very inaccurate if the i.i.d. assumption is violated. We conclude that in many empirical applications the proposed robust bootstrap procedures should routinely replace conventional bootstrap procedures for autoregressions based on the i.i.d. error assumption.

On the dynamic effects of fiscal policy

Dans le sillage de la récession mondiale de 2008-09, plusieurs questions ont été soulevées dans la littérature économique sur les effets à court et à long terme de la politique budgétaire sur l’activité économique par rapport à son signe, sa taille et sa durée. Ceux-ci ont des implications importantes pour mieux comprendre les canaux de transmission et l’efficacité des politiques budgétaires, avec la politique monétaire étant poursuivi, ainsi que pour leurs retombées économiques. Cette thèse fait partie de ce regain d’intérêt de la littérature d’examiner comment les changements dans la politique budgétaire affectent l’activité économique. Elle repose alors sur trois essais: les effets macroéconomiques des chocs de dépenses publiques et des recettes fiscales, les résultats macroéconomiques de l’interaction entre les politiques budgétaire et monétaire et le lien entre la politique budgétaire et la répartition des revenus. Le premier chapitre examine les effets des chocs de politique budgétaire (chocs de dépenses publiques et chocs de recettes fiscales) sur l’économie canadienne au cours de la période 1970-2010, en s’appuyant sur la méthode d’identification des restrictions de signe développée par Mountford et Uhlig [2009]. En réponse à la récession mondiale, les autorités fiscales dans les économies avancées, dont le Canada ont généralement mis en oeuvre une approche en deux phases pour la politique budgétaire. Tout d’abord, ils ont introduit des plans de relance sans précédent pour relancer leurs économies. Par exemple, les mesures de relance au Canada, introduites à travers le Plan d’action économique du Canada, ont été projetées à 3.2 pour cent du PIB dans le budget fédéral de 2009 tandis que l’ "American Recovery and Reinvestment Act"(ARRA) a été estimé à 7 pour cent du PIB. Par la suite, ils ont mis en place des plans d’ajustement en vue de réduire la dette publique et en assurer la soutenabilité à long terme. Dans ce contexte, évaluer les effets multiplicateurs de la politique budgétaire est important en vue d’informer sur l'efficacité de telles mesures dans la relance ou non de l'activité économique. Les résultats montrent que les multiplicateurs d'impôt varient entre 0.2 et 0.5, tandis que les multiplicateurs de dépenses varient entre 0.2 et 1.1. Les multiplicateurs des dépenses ont tendance à être plus grand que les multiplicateurs des recettes fiscales au cours des deux dernières décennies. Comme implications de politique économique, ces résultats tendent à suggérer que les ajustements budgétaires par le biais de grandes réductions de dépenses publiques pourraient être plus dommageable pour l'économie que des ajustements budgétaires par la hausse des impôts. Le deuxième chapitre, co-écrit avec Constant Lonkeng Ngouana, estime les effets multiplicateurs des dépenses publiques aux Etats-Unis en fonction du cycle de la politique monétaire. Les chocs de dépenses publiques sont identifiés comme étant des erreurs de prévision du taux de croissance des dépenses publiques à partir des données d'Enquêtes des prévisionnistes professionnels et des informations contenues dans le "Greenbook". L'état de la politique monétaire est déduite à partir de la déviation du taux des fonds fédéraux du taux cible de la Réserve Fédérale, en faisant recours à une fonction lisse de transition. L'application de la méthode des «projections locales» aux données trimestrielles américaines au cours de la période 1965-2012 suggère que les effets multiplicateurs des dépenses fédérales sont sensiblement plus élevées quand la politique monétaire est accommodante que lorsqu'elle ne l'est pas. Les résultats suggèrent aussi que les dépenses fédérales peuvent stimuler ou non la consommation privée, dépendamment du degré d’accommodation de la politique monétaire. Ce dernier résultat réconcilie ainsi, sur la base d’un cadre unifié des résultats autrement contradictoires à première vue dans la littérature. Ces résultats ont d'importantes implications de politique économique. Ils suggèrent globalement que la politique budgétaire est plus efficace lorsqu'on en a le plus besoin (par exemple, lorsque le taux de chômage est élevé), si elle est soutenue par la politique monétaire. Ils ont également des implications pour la normalisation des conditions monétaires dans les pays avancés: la sortie des politiques monétaires non-conventionnelles conduirait à des multiplicateurs de dépenses fédérales beaucoup plus faibles qu'autrement, même si le niveau de chômage restait élevé. Ceci renforce la nécessité d'une calibration prudente du calendrier de sortie des politiques monétaires non-conventionnelles. Le troisième chapitre examine l'impact des mesures d'expansion et de contraction budgétaire sur la distribution des revenus dans un panel de 18 pays d'Amérique latine au cours de la période 1990-2010, avec un accent sur les deniers 40 pour cent. Il explore alors comment ces mesures fiscales ainsi que leur composition affectent la croissance des revenus des dernier 40 pour cent, la croissance de leur part de revenu ainsi que la croissance économique. Les mesures d'expansion et de contraction budgétaire sont identifiées par des périodes au cours desquels il existe une variation significative du déficit primaire corrigé des variations conjoncturelles en pourcentage du PIB. Les résultats montrent qu'en moyenne l'expansion budgétaire par la hausse des dépenses publiques est plus favorable à la croissance des revenus des moins bien-nantis que celle par la baisse des impôts. Ce résultat est principalement soutenu par la hausse des dépenses gouvernementales de consommation courante, les transferts et subventions. En outre ces mesures d’expansion budgétaire sont favorables à la réduction des inégalités car elles permettent d'améliorer la part des revenus des moins bien-nantis tout en réduisant la part des revenus des mieux-nantis de la distribution des revenus. En outre ces mesures d’expansion budgétaire sont favorables à la réduction des inégalités car elles permettent d'améliorer la part des revenus des moins bien-nantis tout en réduisant la part des revenus des mieux-nantis de la distribution des revenus. Cependant, l'expansion budgétaire pourrait soit n'avoir aucun effet sur la croissance économique ou entraver cette dernière à travers la hausse des dépenses en capital. Les résultats relatifs à la contraction budgétaire sont quelque peu mitigés. Parfois, les mesures de contraction budgétaire sont associées à une baisse de la croissance des revenus des moins bien nantis et à une hausse des inégalités, parfois l'impact de ces mesures est non significatif. Par ailleurs, aucune des mesures n’affecte de manière significative la croissance du PIB. Comme implications de politique économique, les pays avec une certaine marge de manœuvre budgétaire pourraient entamer ou continuer à mettre en œuvre des programmes de "filets de sauvetage"--par exemple les programmes de transfert monétaire conditionnel--permettant aux segments vulnérables de la population de faire face à des chocs négatifs et aussi d'améliorer leur conditions de vie. Avec un potentiel de stimuler l'emploi peu qualifié, une relance budgétaire sage par les dépenses publique courantes pourrait également jouer un rôle important pour la réduction des inégalités. Aussi, pour éviter que les dépenses en capital freinent la croissance économique, les projets d'investissements publics efficients devraient être prioritaires dans le processus d'élaboration des politiques. Ce qui passe par la mise en œuvre des projets d'investissement avec une productivité plus élevée capable de générer la croissance économique nécessaire pour réduire les inégalités.