Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)

O objetivo deste estudo é propor a implementação de um modelo estatístico para cálculo da volatilidade, não difundido na literatura brasileira, o modelo de escala local (LSM), apresentando suas vantagens e desvantagens em relação aos modelos habitualmente utilizados para mensuração de risco. Para estimação dos parâmetros serão usadas as cotações diárias do Ibovespa, no período de janeiro de 2009 a dezembro de 2014, e para a aferição da acurácia empírica dos modelos serão realizados testes fora da amostra, comparando os VaR obtidos para o período de janeiro a dezembro de 2014. Foram introduzidas variáveis explicativas na tentativa de aprimorar os modelos e optou-se pelo correspondente americano do Ibovespa, o índice Dow Jones, por ter apresentado propriedades como: alta correlação, causalidade no sentido de Granger, e razão de log-verossimilhança significativa. Uma das inovações do modelo de escala local é não utilizar diretamente a variância, mas sim a sua recíproca, chamada de “precisão” da série, que segue uma espécie de passeio aleatório multiplicativo. O LSM captou todos os fatos estilizados das séries financeiras, e os resultados foram favoráveis a sua utilização, logo, o modelo torna-se uma alternativa de especificação eficiente e parcimoniosa para estimar e prever volatilidade, na medida em que possui apenas um parâmetro a ser estimado, o que representa uma mudança de paradigma em relação aos modelos de heterocedasticidade condicional.

Automatic model selection for forecasting brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We nd that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Automatic model selection for forecasting Brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We find that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Speculative bubbles and contagion: analysis of volatility's clusters during the DotCom bubble based on the dynamic conditional correlation model

Reviewing the de nition and measurement of speculative bubbles in context of contagion, this paper analyses the DotCom bubble in American and European equity markets using the dynamic conditional correlation (DCC) model proposed by (Engle and Sheppard 2001) as on one hand as an econometrics explanation and on the other hand the behavioral nance as an psychological explanation. Contagion is de ned in this context as the statistical break in the computed DCCs as measured by the shifts in their means and medians. Even it is astonishing, that the contagion is lower during price bubbles, the main nding indicates the presence of contagion in the di¤erent indices among those two continents and proves the presence of structural changes during nancial crisis

A simple and non-destructive model for individual leaf area estimation in citrus

Introduction. Leaf area is often related to plant growth, development, physiology and yield. Many non-destructive models have been proposed for leaf area estimation of several plant genotypes, demonstrating that leaf length, leaf width and leaf area are closely correlated. Thus, the objective of our study was to develop a reliable model for leaf area estimation from linear measurements of leaf dimensions for citrus genotypes. Materials and methods. Leaves of citrus genotypes were harvested, and their dimensions (length, width and area) were measured. Values of leaf area were regressed against length, width, the square of length, the square of width and the product (length x width). The most accurate equations, either linear or second-order polynomial, were regressed again with a new data set; then the most reliable equation was defined. Results and discussion. The first analysis showed that the variables length, width and the square of length gave better results in second-order polynomial equations, while the linear equations were more suitable and accurate when the width and the product (length x width) were used. When these equations were regressed with the new data set, the coefficient of determination (R(2)) and the agreement index 'd' were higher for the one that used the variable product (length x width), while the Mean Absolute Percentage Error was lower. Conclusion. The product of the simple leaf dimensions (length x width) can provide a reliable and simple non-destructive model for leaf area estimation across citrus genotypes.

Um sistema para estimação da vazão de gás de poços produzindo por Plunger Lift para vaso separador de teste em plataformas de petróleo

This work intends to analyze the behavior of the gas flow of plunger lift wells producing to well testing separators in offshore production platforms to aim a technical procedure to estimate the gas flow during the slug production period. The motivation for this work appeared from the expectation of some wells equipped with plunger lift method by PETROBRAS in Ubarana sea field located at Rio Grande do Norte State coast where the produced fluids measurement is made in well testing separators at the platform. The oil artificial lift method called plunger lift is used when the available energy of the reservoir is not high enough to overcome all the necessary load losses to lift the oil from the bottom of the well to the surface continuously. This method consists, basically, in one free piston acting as a mechanical interface between the formation gas and the produced liquids, greatly increasing the well s lifting efficiency. A pneumatic control valve is mounted at the flow line to control the cycles. When this valve opens, the plunger starts to move from the bottom to the surface of the well lifting all the oil and gas that are above it until to reach the well test separator where the fluids are measured. The well test separator is used to measure all the volumes produced by the well during a certain period of time called production test. In most cases, the separators are designed to measure stabilized flow, in other words, reasonably constant flow by the use of level and pressure electronic controllers (PLC) and by assumption of a steady pressure inside the separator. With plunger lift wells the liquid and gas flow at the surface are cyclical and unstable what causes the appearance of slugs inside the separator, mainly in the gas phase, because introduce significant errors in the measurement system (e.g.: overrange error). The flow gas analysis proposed in this work is based on two mathematical models used together: i) a plunger lift well model proposed by Baruzzi [1] with later modifications made by Bolonhini [2] to built a plunger lift simulator; ii) a two-phase separator model (gas + liquid) based from a three-phase separator model (gas + oil + water) proposed by Nunes [3]. Based on the models above and with field data collected from the well test separator of PUB-02 platform (Ubarana sea field) it was possible to demonstrate that the output gas flow of the separator can be estimate, with a reasonable precision, from the control signal of the Pressure Control Valve (PCV). Several models of the System Identification Toolbox from MATLAB® were analyzed to evaluate which one better fit to the data collected from the field. For validation of the models, it was used the AIC criterion, as well as a variant of the cross validation criterion. The ARX model performance was the best one to fit to the data and, this way, we decided to evaluate a recursive algorithm (RARX) also with real time data. The results were quite promising that indicating the viability to estimate the output gas flow rate from a plunger lift well producing to a well test separator, with the built-in information of the control signal to the PCV

Improving the GNSS positioning stochastic model in the presence of ionospheric scintillation

Ionospheric scintillations are caused by time-varying electron density irregularities in the ionosphere, occurring more often at equatorial and high latitudes. This paper focuses exclusively on experiments undertaken in Europe, at geographic latitudes between similar to 50 degrees N and similar to 80 degrees N, where a network of GPS receivers capable of monitoring Total Electron Content and ionospheric scintillation parameters was deployed. The widely used ionospheric scintillation indices S4 and sigma(phi) represent a practical measure of the intensity of amplitude and phase scintillation affecting GNSS receivers. However, they do not provide sufficient information regarding the actual tracking errors that degrade GNSS receiver performance. Suitable receiver tracking models, sensitive to ionospheric scintillation, allow the computation of the variance of the output error of the receiver PLL (Phase Locked Loop) and DLL (Delay Locked Loop), which expresses the quality of the range measurements used by the receiver to calculate user position. The ability of such models of incorporating phase and amplitude scintillation effects into the variance of these tracking errors underpins our proposed method of applying relative weights to measurements from different satellites. That gives the least squares stochastic model used for position computation a more realistic representation, vis-a-vis the otherwise 'equal weights' model. For pseudorange processing, relative weights were computed, so that a 'scintillation-mitigated' solution could be performed and compared to the (non-mitigated) 'equal weights' solution. An improvement between 17 and 38% in height accuracy was achieved when an epoch by epoch differential solution was computed over baselines ranging from 1 to 750 km. The method was then compared with alternative approaches that can be used to improve the least squares stochastic model such as weighting according to satellite elevation angle and by the inverse of the square of the standard deviation of the code/carrier divergence (sigma CCDiv). The influence of multipath effects on the proposed mitigation approach is also discussed. With the use of high rate scintillation data in addition to the scintillation indices a carrier phase based mitigated solution was also implemented and compared with the conventional solution. During a period of occurrence of high phase scintillation it was observed that problems related to ambiguity resolution can be reduced by the use of the proposed mitigated solution.

A Proposal for System Performance Evaluation for an ERP Knowledge Extraction Model

Much has been researched and discussed in the importance played by knowledge in organizations. We are witnessing the establishment of the knowledge economy, but this "new economy" brings in itself a whole complex system of metrics and evaluations, and cannot be dissociated from it. Due to its importance, the initiatives of knowledge management must be continually assessed on their progress in order to verify whether they are moving towards achieving the goals of success. Thus, good measurement practices should include not only how the organization quantifies its knowledge capital, but also how resources are allocated to supply their growth. Thinking about the aspects listed above, this paper presents an approach to a model for Knowledge extraction using an ERP system, suggesting the establishment of a set of indicators for assessing organizational performance. The objective is to evaluate the implementation of projects of knowledge management and thus observe the general development of the organization.

A New Approach for Real Time Fault Diagnosis in Induction Motors Based on Vibration Measurement

Condition monitoring is used to increase machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient real time vibration measurement and analysis instruments is capable of providing warning and predicting faults at early stages. In this paper, a new methodology for the implementation of vibration measurement and analysis instruments in real time based on circuit architecture mapped from a MATLAB/Simulink model is presented. In this study, signal processing applications such as FIR filters and fast Fourier transform are treated as systems, which are implemented in hardware using a system generator toolbox, which translates a Simulink model in a hardware description language - HDL for FPGA implementations.

Viscosity measurement of Newtonian liquids using the complex reflection coefficient

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

High sensitivity fiber optic angular displacement sensor and its application for detection of ultrasound

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.

Model for the Evaluation of Root Wear by Histometric Analysis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)