Determinantes da sustentabilidade financeira em diversos níveis de governo

Dissertação de Mestrado, Ciências Económicas e Empresariais, 18 de Julho de 2016, Universidade dos Açores.

EL "PASS THROUGH" DEL TIPO DE CAMBIO EN LOS PRECIOS DE BIENES TRANSABLES Y NO TRANSABLES EN COSTA RICA

Este trabajo estima el coeficiente de pass through del tipo de cambio en los precios de bienes transables y no transables en Costa Rica, para el corto y el largo plazo. Se utiliza el análisis de mínimos cuadrados para estimar los coeficientes, y se explora la dinámica de ajuste de los modelos utilizando el análisis de vectores auto regresivo. Dentro de los principales resultados del modelo se encontró un coeficiente de pass through para los bienes transables de 13% en el corto plazo y de 68% en el largo plazo; para los bienes no transables, el pass through es de 10% y 52% en el corto y largo plazo respectivamente. En el largo plazo se incluye un 7% de pass through indirecto producto del efecto de los precios de los transables en los de no transables. El estudio de la dinámica de ajuste de los precios de transables y no transables ante un choque del tipo de cambio mostró una duración de 17 y 27 meses respectivamente. Además se realizaron pruebas de causalidad de Granger y estabilidad del modelo. La primera mostró una relación de precedencia entre las variaciones de tipo de cambio e inflación, y entre inflación de los transables y de los no transables. La segunda evidencia un cambio estructural en el modelo de los no transables entre fines de 1995 e inicio de 1996. AbstractThis paper estimates short run and long run coefficients of exchange rate pass through in to the prices of tradable and non tradable goods in Costa Rica. The coefficients are estimated by OLS. A VAR analysis is conducted in order to estimate the dynamic process between exchange rate and inflation. Granger causality test and a stability test are conducted too. The short run pass through coefficients are 13% and 10%, for tradable and non tradable goods respectively and the long run coefficients are 68% and 52% in the same order. There is a second stage pass through of 7% included in the long run coefficient for non tradable goods. The dynamic analysis shows that the adjustment process of prices as a result of an exchange rate shock takes 17 months for tradable goods and 27 months for non tradable goods. The Granger causality test shows precedence between variation in the exchange rate and inflation, and between the prices of tradable and non tradable goods. There is statistical evidence of a structural change in the non tradable model between the end of 1995 and the beginning of 1996.

Do increases in oil prices precede U.S. recessions?

This thesis examines the relationship between oil prices and economic activity, and it attempts to address the question: do increases in oil prices (oil shocks) precede U.S. recessions? This paper also applied macroeconomics, either through the direct use of a macroeconomic point of view or using a combination of mathematical and statistical models. Two mathematical and statistical models are used to determine the ability of oil prices to predict recessions in the United States. First, using the binary cyclical (Bry-Boschan method) indicator procedure to test the turning point of oil prices compared with turning points in GDP finds that oil prices almost always turn five month before a recession, suggesting that an oil shock might occur before a recession. Second, the Granger causality test shows that oil prices change do Granger cause U.S. recessions, indicating that oil prices are a useful signal to indicate a U.S. recession. Finally, combining this analysis with the literature, there are several potential explanations that the spike in oil prices result in slower GDP growth and are a contributing factor to U.S. recessions.

Causality and Contagion in EMU Sovereign Debt Markets

This paper contributes to the literature by applying the Granger causality approach and endogenous breakpoint test to offer an operational definition of contagion to examine European Economic and Monetary Union (EMU) countries public debt behaviour. A database of yields on 10-year government bonds issued by 11 EMU countries covering fourteen years of monetary union is used. The main results suggest that the 41 new causality patterns, which appeared for the first time in the crisis period, and the intensification of causality recorded in 70% of the cases, provide clear evidence of contagion in the aftermath of the current euro debt crisis.

Causality and Contagion in EMU Sovereign Debt Markets

This paper contributes to the literature by applying the Granger causality approach and endogenous breakpoint test to offer an operational definition of contagion to examine European Economic and Monetary Union (EMU) countries public debt behaviour. A database of yields on 10-year government bonds issued by 11 EMU countries covering fourteen years of monetary union is used. The main results suggest that the 41 new causality patterns, which appeared for the first time in the crisis period, and the intensification of causality recorded in 70% of the cases, provide clear evidence of contagion in the aftermath of the current euro debt crisis.

Causality and Contagion in EMU Sovereign Debt Markets

This paper contributes to the literature by applying the Granger causality approach and endogenous breakpoint test to offer an operational definition of contagion to examine European Economic and Monetary Union (EMU) countries public debt behaviour. A database of yields on 10-year government bonds issued by 11 EMU countries covering fourteen years of monetary union is used. The main results suggest that the 41 new causality patterns, which appeared for the first time in the crisis period, and the intensification of causality recorded in 70% of the cases, provide clear evidence of contagion in the aftermath of the current euro debt crisis.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Causas do desmatamento da Amazônia: uma aplicação do teste de causalidade de Granger acerca das principais fontes de desmatamento nos municípios da Amazônia Legal brasileira

Muitas são os fatores, apontadas pela literatura pertinente, acerca das causas do desmatamento da Amazônia Legal brasileira. Desde aspectos endógenos como as condições edafo-climáticas, a aspectos relacionados à ação antrópica como os movimentos populacionais, o crescimento urbano e, em especial, as ações autônomas ou induzidas dos diversos agentes econômicos públicos e privados que têm atuado na região, configurando historicamente os processos de ocupação do solo e aproveitamento econômico do espaço amazônico. Este artigo tem como objetivo realizar um teste de causalidade, no sentido de Granger, nas principais variáveis sugeridas como importantes para explicar o desmatamento da Amazônia Legal, no período de 1997 a 2006. A metodologia a ser empregada se baseia em modelos dinâmicos para dados em painel, desenvolvidos por Holtz-Eakin et al. (1988) e Arellano-Bond (1991), que desenvolveram um teste de causalidade baseado no artigo seminal de Granger (1969). Entre os principais resultados obtidos está a constatação empírica de que existe uma causalidade bidirecional entre desmatamento e as áreas de culturas permanente e temporária, bem como o tamanho do rebanho bovino.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Financial leverage and tangibility of assets of Brazilian companies of agribusiness in the post-real plan period

This paper aims to study the relationship between the debt level and the asset structure of Brazilian companies of the agribusiness sector, since it is considered a current and relevant discussion: to evaluate the mechanisms for fund-raising and guarantees. The methodology of Granger`s Causality test and Autoregressive Vectors was used to conduct a comparative analysis, applied to a financial database of companies with open capital of Brazilian agribusiness, in particular the agricultural sector and Fisheries and Food and Beverages in a period of 10 years (1997-2007) from quarterly series available in the database of Economatica(R). The results demonstrated that changes in leverage generate variations in the tangibility of the companies, a fact that can be explained by the large search of funding secured by fiduciary transfer of fixed assets, which facilitates access to credit by business of the Agribusiness sector, increasing the payment time and lowering interest rates.

Monte Carlo evidence on cointegration and causation

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.

Avaliação de dados funcionais em repouso do cérebro normal: causalidade de Granger

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

On the dynamics of exports and FDI: The Spanish Internationalization Process

This paper provides further insights into the dynamics of exports and outward foreign direct investment (FDI) flows in Spain from a time-series approach. The contribution of the paper is twofold: 1) the existence of either substitution or a complementary relationship between Spanish outward investments and exports is empirically tested using a multivariate cointegrated model (VECM). The evolution in exchange flows (1993-2008) and country-specific variables (such as world demand - including Spain’s main recently growing foreign markets - for trade flows and the relative price of exports in order to proxy new global competitors) are taken into account for the first time. And 2) the growth in the trade of services in recent decades leads us to test a specific causality relationship by disaggregating between goods and services flows. Our results provide evidence of a positive (Granger) causality relationship running from FDI to exports of goods (stronger) and to exports of services (weaker) in the long run, the complementarity relation of which is consistent with vertical FDI strategies. In the short run, however, only exports of goods are affected (positively) by FDIs.

The Fed Model: International Analysis

The Fed model is a widely used market valuation model. It is often used only on market analysis of the S&P 500 index as a shorthand measure for the attractiveness of equity, and as a timing device for allocating funds between equity and bonds. The Fed model assumes a fixed relationship between bond yield and earnings yield. This relationship is often assumed to be true in market valuation. In this paper we test the Fed model from historical perspective on the European markets. The markets of the United States are also includedfor comparison. The purpose of the tests is to determine if the Fed model and the underlying assumptions come true on different markets. The various tests are made on time-series data ranging from the year 1973 to the end of the year 2008. The statistical methods used are regressions analysis, cointegration analysis and Granger causality. The empirical results do not give strong support for the Fed model. The underlying relationships assumed by the Fed model are statistically not valid in most of the markets examined and therefore the model is not valid in valuation purposes generally. The results vary between the different markets which gives reason to suspect the general use of the Fed model in different market conditions and in different markets.