The finance-welfare state nexus. ACES Cases No. 2013.1

At the height of the financial crisis, the Western welfare state prevented a repeat of the Great Depression. But there were also suggestions that social policy had contributed to the crisis, particularly by promoting households’ access to credit in pursuit of welfare goals. Others claim that it was the withdrawal of state welfare that led to the disaster. Against this background that motivated our interest, we propose a systematic way of assessing the relationship between financial market and public welfare provisions. We use structural vector auto-regression to establish the causal link and its direction. Two hypotheses about this relationship can be inferred from the literature. First, the notion that welfare states ‘decommodify’ livelihoods or that there is an equity-efficiency tradeoff would suggest that welfare states substitute to varying degrees for financial market offers of insurance and savings. By contrast, welfare states may support private interests selectively and/or help markets for households to function better; thus the nexus would be one of complementarity. Our empirical strategy is to spell out the causal mechanisms that can account for a substitutive or complementary relationship and then to see whether advanced econometric techniques find evidence for the existence of either of these mechanisms in six OECD countries. We find complementarity between public welfare (spending and tax subsidies) and life insurance markets for four out of our six countries, notably even for the United States. Substitution between welfare and finance is the more plausible interpretation for France and the Netherlands, which is surprising. Data availability constrains us from testing the implications for the welfare state contribution to the crisis directly but our findings suggest that the welfare state cannot generally be blamed for the financial crisis.

A dinâmica da taxa de câmbio no Brasil face às operações swap (2002-2015)

This dissertation investigates, based on the Post-Keynesian theory and on its concept of monetary economy of production, the exchange rate behavior of the Brazilian Real in the presence of Brazilian Central Bank's interventions by means of the so-called swap transactions over 2002-2015. Initially, the work analyzes the essential properties of an open monetary economy of production and, thereafter, it presents the basic propositions of the Post-Keynesian view on the exchange rate determination, highlighting the properties of foreign exchange markets and the peculiarities of the Brazilian position into the international monetary and financial system. The research, thereby, accounts for the various segments of the Brazilian foreign exchange market. To accomplish its purpose, we first do a literature review of the Post-Keynesian literature about the topic. Then, we undertake empirical exams of the exchange rate determination using two statistical methods. On the one hand, to measure the volatility of exchange rate, we estimate Auto-regressive Conditional Heteroscedastic (ARCH) and Generalized Auto-regressive Conditional Heteroscedastic (GARCH) models. On the other hand, to measure the variance of the exchange rate in relation to real, financial variables, and the swaps, we estimate a Vector Auto-regression (VAR) model. Both experiments are performed for the nominal and real effective exchange rates. The results show that the swaps respond to exchange rate movements, trying to offset its volatility. This reveals that the exchange rate is, at least in a certain magnitude, sensitive to swaps transactions conducted by the Central Bank. In addition, another empirical result is that the real effective exchange rate responds more to the swaps auctions than the nominal rate.

Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.

Causality analysis of groundwater dynamics based on a Vector Autoregressive model in the semi-arid basin of Gundal (South India)

Causal relationships existing between observed levels of groundwater in a semi-arid sub-basin of the Kabini River basin (Karnataka state, India) are investigated in this study. A Vector Auto Regressive model is used for this purpose. Its structure is built on an upstream/downstream interaction network based on observed hydro-physical properties. Exogenous climatic forcing is used as an input based on cumulated rainfall departure. Optimal models are obtained thanks to a trial approach and are used as a proxy of the dynamics to derive causal networks. It appears to be an interesting tool for analysing the causal relationships existing inside the basin. The causal network reveals 3 main regions: the Northeastern part of the Gundal basin is closely coupled to the outlet dynamics. The Northwestern part is mainly controlled by the climatic forcing and only marginally linked to the outlet dynamic. Finally, the upper part of the basin plays as a forcing rather than a coupling with the lower part of the basin allowing for a separate analysis of this local behaviour. The analysis also reveals differential time scales at work inside the basin when comparing upstream oriented with downstream oriented causalities. In the upper part of the basin, time delays are close to 2 months in the upward direction and lower than 1 month in the downward direction. These time scales are likely to be good indicators of the hydraulic response time of the basin which is a parameter usually difficult to estimate practically. This suggests that, at the sub-basin scale, intra-annual time scales would be more relevant scales for analysing or modelling tropical basin dynamics in hard rock (granitic and gneissic) aquifers ubiquitous in south India. (c) 2012 Elsevier B.V. All rights reserved.

Rational expectations? Developer behaviour and development cycles in the central London office market

Purpose – Expectations of future market conditions are acknowledged to be crucial for the development decision and hence for shaping the built environment. The purpose of this paper is to study the central London office market from 1987 to 2009 and test for evidence of rational, adaptive and naive expectations. Design/methodology/approach – Two parallel approaches are applied to test for either rational or adaptive/naive expectations: vector auto-regressive (VAR) approach with Granger causality tests and recursive OLS regression with one-step forecasts. Findings – Applying VAR models and a recursive OLS regression with one-step forecasts, the authors do not find evidence of adaptive and naïve expectations of developers. Although the magnitude of the errors and the length of time lags between market signal and construction starts vary over time and development cycles, the results confirm that developer decisions are explained, to a large extent, by contemporaneous and historic conditions in both the City and the West End, but this is more likely to stem from the lengthy design, financing and planning permission processes rather than adaptive or naive expectations. Research limitations/implications – More generally, the results of this study suggest that real estate cycles are largely generated endogenously rather than being the result of large demand shocks and/or irrational behaviour. Practical implications – Developers may be able to generate excess profits by exploiting market inefficiencies but this may be hindered in practice by the long periods necessary for planning and construction of the asset. Originality/value – This paper focuses the scholarly debate of real estate cycles on the role of expectations. It is also one of very few spatially disaggregate studies of the subject matter.

REGRESSION ANALYSIS FOR STANDARDIZED MORTALITY RATIOS WITH APPLICATION TO OCCUPATIONAL FOLLOW-UP STUDIES

Traditional comparison of standardized mortality ratios (SMRs) can be misleading if the age-specific mortality ratios are not homogeneous. For this reason, a regression model has been developed which incorporates the mortality ratio as a function of age. This model is then applied to mortality data from an occupational cohort study. The nature of the occupational data necessitates the investigation of mortality ratios which increase with age. These occupational data are used primarily to illustrate and develop the statistical methodology.^ The age-specific mortality ratio (MR) for the covariates of interest can be written as MR(,ij...m) = ((mu)(,ij...m)/(theta)(,ij...m)) = r(.)exp (Z('')(,ij...m)(beta)) where (mu)(,ij...m) and (theta)(,ij...m) denote the force of mortality in the study and chosen standard populations in the ij...m('th) stratum, respectively, r is the intercept, Z(,ij...m) is the vector of covariables associated with the i('th) age interval, and (beta) is a vector of regression coefficients associated with these covariables. A Newton-Raphson iterative procedure has been used for determining the maximum likelihood estimates of the regression coefficients.^ This model provides a statistical method for a logical and easily interpretable explanation of an occupational cohort mortality experience. Since it gives a reasonable fit to the mortality data, it can also be concluded that the model is fairly realistic. The traditional statistical method for the analysis of occupational cohort mortality data is to present a summary index such as the SMR under the assumption of constant (homogeneous) age-specific mortality ratios. Since the mortality ratios for occupational groups usually increase with age, the homogeneity assumption of the age-specific mortality ratios is often untenable. The traditional method of comparing SMRs under the homogeneity assumption is a special case of this model, without age as a covariate.^ This model also provides a statistical technique to evaluate the relative risk between two SMRs or a dose-response relationship among several SMRs. The model presented has application in the medical, demographic and epidemiologic areas. The methods developed in this thesis are suitable for future analyses of mortality or morbidity data when the age-specific mortality/morbidity experience is a function of age or when there is an interaction effect between confounding variables needs to be evaluated. ^

Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.

A Cointegration Approach to Topics in Empirical Macroeconomics (summary section only)

Mikael Juselius’ doctoral dissertation covers a range of significant issues in modern macroeconomics by empirically testing a number of important theoretical hypotheses. The first essay presents indirect evidence within the framework of the cointegrated VAR model on the elasticity of substitution between capital and labor by using Finnish manufacturing data. Instead of estimating the elasticity of substitution by using the first order conditions, he develops a new approach that utilizes a CES production function in a model with a 3-stage decision process: investment in the long run, wage bargaining in the medium run and price and employment decisions in the short run. He estimates the elasticity of substitution to be below one. The second essay tests the restrictions implied by the core equations of the New Keynesian Model (NKM) in a vector autoregressive model (VAR) by using both Euro area and U.S. data. Both the new Keynesian Phillips curve and the aggregate demand curve are estimated and tested. The restrictions implied by the core equations of the NKM are rejected on both U.S. and Euro area data. These results are important for further research. The third essay is methodologically similar to essay 2, but it concentrates on Finnish macro data by adopting a theoretical framework of an open economy. Juselius’ results suggests that the open economy NKM framework is too stylized to provide an adequate explanation for Finnish inflation. The final essay provides a macroeconometric model of Finnish inflation and associated explanatory variables and it estimates the relative importance of different inflation theories. His main finding is that Finnish inflation is primarily determined by excess demand in the product market and by changes in the long-term interest rate. This study is part of the research agenda carried out by the Research Unit of Economic Structure and Growth (RUESG). The aim of RUESG it to conduct theoretical and empirical research with respect to important issues in industrial economics, real option theory, game theory, organization theory, theory of financial systems as well as to study problems in labor markets, macroeconomics, natural resources, taxation and time series econometrics. RUESG was established at the beginning of 1995 and is one of the National Centers of Excellence in research selected by the Academy of Finland. It is financed jointly by the Academy of Finland, the University of Helsinki, the Yrjö Jahnsson Foundation, Bank of Finland and the Nokia Group. This support is gratefully acknowledged.

Estudo da previsão de propriedades do biodiesel utilizando espectros de infravermelho e calibração multivariada

O biodiesel tem sido amplamente utilizado como uma fonte de energia renovável, que contribui para a diminuição de demanda por diesel mineral. Portanto, existem várias propriedades que devem ser monitoradas, a fim de produzir e distribuir biodiesel com a qualidade exigida. Neste trabalho, as propriedades físicas do biodiesel, tais como massa específica, índice de refração e ponto de entupimento de filtro a frio foram medidas e associadas a espectrometria no infravermelho próximo (NIR) e espectrometria no infravermelho médio (Mid-IR) utilizando ferramentas quimiométricas. Os métodos de regressão por mínimos quadrados parciais (PLS), regressão de mínimos quadrados parciais por intervalos (iPLS), e regressão por máquinas de vetor de suporte (SVM) com seleção de variáveis por Algoritmo Genético (GA) foram utilizadas para modelar as propriedades mencionadas. As amostras de biodiesel foram sintetizadas a partir de diferentes fontes, tais como canola, girassol, milho e soja. Amostras adicionais de biodiesel foram adquiridas de um fornecedor da região sul do Brasil. Em primeiro lugar, o pré-processamento de correção de linha de base foi usado para normalizar os dados espectrais de NIR, seguidos de outros tipos de pré-processamentos que foram aplicados, tais como centralização dos dados na média, 1 derivada e variação de padrão normal. O melhor resultado para a previsão do ponto de entupimento de filtro a frio foi utilizando os espectros de Mid-IR e o método de regressão GA-SVM, com alto coeficiente de determinação da previsão, R2Pred=0,96 e baixo valor da Raiz Quadrada do Erro Médio Quadrático da previsão, RMSEP (C)= 0,6. Para o modelo de previsão da massa específica, o melhor resultado foi obtido utilizando os espectros de Mid-IR e regressão por PLS, com R2Pred=0,98 e RMSEP (g/cm3)= 0,0002. Quanto ao modelo de previsão para o índice de refração, o melhor resultado foi obtido utilizando os espectros de Mid-IR e regressão por PLS, com excelente R2Pred=0,98 e RMSEP= 0,0001. Para esses conjuntos de dados, o PLS e o SVM demonstraram sua robustez, apresentando-se como ferramentas úteis para a previsão das propriedades do biodiesel estudadas

Estudo de níveis de ozônio troposférico usando métodos de estatística univariada e multivariada para duas localidades da cidade do Rio de Janeiro

Com cada vez mais intenso desenvolvimento urbano e industrial, atualmente um desafio fundamental é eliminar ou reduzir o impacto causado pelas emissões de poluentes para a atmosfera. No ano de 2012, o Rio de Janeiro sediou a Rio +20, a Conferência das Nações Unidas sobre Desenvolvimento Sustentável, onde representantes de todo o mundo participaram. Na época, entre outros assuntos foram discutidos a economia verde e o desenvolvimento sustentável. O O3 troposférico apresenta-se como uma variável extremamente importante devido ao seu forte impacto ambiental, e conhecer o comportamento dos parâmetros que afetam a qualidade do ar de uma região, é útil para prever cenários. A química das ciências atmosféricas e meteorologia são altamente não lineares e, assim, as previsões de parâmetros de qualidade do ar são difíceis de serem determinadas. A qualidade do ar depende de emissões, de meteorologia e topografia. Os dados observados foram o dióxido de nitrogênio (NO2), monóxido de nitrogênio (NO), óxidos de nitrogênio (NOx), monóxido de carbono (CO), ozônio (O3), velocidade escalar vento (VEV), radiação solar global (RSG), temperatura (TEM), umidade relativa (UR) e foram coletados através da estação móvel de monitoramento da Secretaria do Meio Ambiente (SMAC) do Rio de Janeiro em dois locais na área metropolitana, na Pontifícia Universidade Católica (PUC-Rio) e na Universidade do Estado do Rio de Janeiro (UERJ) no ano de 2011 e 2012. Este estudo teve três objetivos: (1) analisar o comportamento das variáveis, utilizando o método de análise de componentes principais (PCA) de análise exploratória, (2) propor previsões de níveis de O3 a partir de poluentes primários e de fatores meteorológicos, comparando a eficácia dos métodos não lineares, como as redes neurais artificiais (ANN) e regressão por máquina de vetor de suporte (SVM-R), a partir de poluentes primários e de fatores meteorológicos e, finalmente, (3) realizar método de classificação de dados usando a classificação por máquina de vetor suporte (SVM-C). A técnica PCA mostrou que, para conjunto de dados da PUC as variáveis NO, NOx e VEV obtiveram um impacto maior sobre a concentração de O3 e o conjunto de dados da UERJ teve a TEM e a RSG como as variáveis mais importantes. Os resultados das técnicas de regressão não linear ANN e SVM obtidos foram muito próximos e aceitáveis para o conjunto de dados da UERJ apresentando coeficiente de determinação (R2) para a validação, 0,9122 e 0,9152 e Raiz Quadrada do Erro Médio Quadrático (RMECV) 7,66 e 7,85, respectivamente. Quanto aos conjuntos de dados PUC e PUC+UERJ, ambas as técnicas, obtiveram resultados menos satisfatórios. Para estes conjuntos de dados, a SVM mostrou resultados ligeiramente superiores, e PCA, SVM e ANN demonstraram sua robustez apresentando-se como ferramentas úteis para a compreensão, classificação e previsão de cenários da qualidade do ar

Modern spectral estimation of ICP-AES

The inductively coupled plasma atomic emission, spectrometry (ICP-AES) and its signal characteristics were discussed using modem spectral estimation technique. The power spectra density (PSD) was calculated using the auto-regression (AR) model of modem spectra estimation. The Levinson-Durbin recursion method was used to estimate the model parameters which were used for the PSD computation. The results obtained with actual ICP-AES spectra and measurements showed that the spectral estimation technique was helpful for the better understanding about spectral composition and signal characteristics.

The response of phytoplankton to climate variability associated with the North Atlantic Oscillation

Inter-annual variability in the timing of phytoplankton spring bloom and phytoplankton community structure in the central North Atlantic Ocean was quantified using ocean color data and continuous plankton recorder (CPR) data. This variability was related to the North Atlantic Oscillation using correlation analysis and multivariate auto-regression models. The initiation of the spring bloom derived from CPR phytoplankton color index data is similar to that derived from satellite chlorophyll, and exhibits a nominal correlation with the sea surface temperature (SST) and the North Atlantic Oscillation (NAO). The extrapolated spring bloom timing suggested later initiation of blooms in the mid-1980s and earlier initiation of blooms in the 1990s. The climatological phytoplankton community structure in the central North Atlantic is dominated by diatoms, except for a shift in community composition favoring dinoflagellates in August. The ratio of diatoms to total phytoplankton abundance and the ratio of dinoflagellates to total phytoplankton abundance are both closely correlated with the NAO and SST. The extended time series of phytoplankton community structure between 1985 and 2009, deduced from the time series of SST and NAO over the same interval, showed a decadal shift away from diatoms towards dinoflagellates. The linkages between the NAO, and changes in stratification and phytoplankton processes occur over a larger scale than previously observed.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Structure, conduct and the stochastic volatility of food markets: theory and empirics

The relationship between price volatility and competition is examined. Atheoretic, vector auto regressions on farm prices of wheat and retail prices of derivatives (flour, bread, pasta, bulgur and cookies) are compared to results from a dynamic, simultaneous-equations model with theory-based farm-to-retail linkages. Analytical results yield insights about numbers of firms and their impacts on demand- and supply-side multipliers, but the applications to Turkish time series (1988:1-1996:12) yield mixed results.