Demand and supply: price elasticities and corporate performance in the UK

This paper investigates the relationship between consumer demand and corporate performance in several consumer industries in the UK, using two independent datasets. It uses data on consumer expenditures and the retail price index to estimate Almost Ideal Demand Systems on micro-data and compute timevarying price elasticities of demand for disaggregated commodity groups. Then, it matches the product definitions to the Standard Industry Classification and uses the estimated elasticities to investigate the impact of consumer behaviour on firm-level profitability equations. The time-varying household characteristics are ideal instruments for the demand effects in the firms' supply equation. The paper concludes that demand elasticities have a significant and tangible impact on the profitability of UK firms and that this impact can shed some light on the relationship between market structure and economic performance.

Especulative (sic) attack on exchange rate target zone regime : the uncertainty case

We present a continuous time target zone model of speculative attacks. Contrary to most of the literature that considers the certainty case, i.e., agents know for sure the Central Bank behavior in the future, we build uncertainty into the madel in two different ways. First, we consider the case in whicb the leveI of reserves at which the central bank lets the regime collapse is uncertain. Alternatively, we ana1ize the case in which, with some probability, the government may cbange its policy reducing the initially positive trend in domestic credito In both cases, contrary to the case of a fixed exchange rate regime, speculators face a cost of launching a tentative attack that may not succeed. Such cost induces a delay and may even prevent its occurrence. At the time of the tentative attack, the exchange rate moves either discretely up, if the attack succeeds, or down, if it fails. The remlts are consistent with the fact that, typically, an attack involves substantial profits and losses for the speculators. In particular, if agents believed that the government will control fiscal imbalances in the future, or alternatively, if they believe the trend in domestic credit to be temporary, the attack is postponed even in the presence of a signal of an imminent collapse. Finally, we aIso show that the timing of a speculative attack increases with the width of the target zone.

Testing covariance stationarity

In this paper, we show that the widely used stationarity tests such as the KPSS test has power close to size in the presence of time-varying unconditional variance. We propose a new test as a complement of the existing tests. Monte Carlo experiments show that the proposed test possesses the following characteristics: (i) In the presence of unit root or a structural change in the mean, the proposed test is as powerful as the KPSS and other tests; (ii) In the presence a changing variance, the traditional tests perform badly whereas the proposed test has high power comparing to the existing tests; (iii) The proposed test has the same size as traditional stationarity tests under the null hypothesis of covariance stationarity. An application to daily observations of return on US Dollar/Euro exchange rate reveals the existence of instability in the unconditional variance when the entire sample is considered, but stability is found in sub-samples.

A restatement of the welafare costs of inflation. The wast of scarce resources in the manufacturing , banking and household sectors

Lucas (2000) estimates that the US welfare costs of inflation are around 1% of GDP. This measurement is consistent with a speci…c distorting channel in terms of the Bailey triangle under the demand for monetary base schedule (outside money): the displacement of resources from the production of consumption goods to the household transaction time à la Baumol. Here, we consider also several new types of distortions in the manufacturing and banking industries. Our new evidences show that both banks and firms demand special occupational employments to avoid the inflation tax. We de…ne the concept of ”the foat labor”: The occupational employments that are aflected by the in‡ation rates. More administrative workers are hired relatively to the bluecollar workers for producing consumption goods. This new phenomenon makes the manufacturing industry more roundabout. To take into account this new stylized fact and others, we redo at same time both ”The model 5: A Banking Sector -2” formulated by Lucas (1993) and ”The Competitive Banking System” proposed by Yoshino (1993). This modelling allows us to characterize better the new types of misallocations. We …nd that the maximum value of the resources wasted by the US economy happened in the years 1980-81, after the 2nd oil shock. In these years, we estimate the excess resources that are allocated for every speci…c distorting channel: i) The US commercial banks spent additional resources of around 2% of GDP; ii) For the purpose of the firm foating time were used between 2.4% and 4.1% of GDP); and iii) For the household transaction time were allocated between 3.1% and 4.5 % of GDP. The Bailey triangle under the demand for the monetary base schedule represented around 1% of GDP, which is consistent with Lucas (2000). We estimate that the US total welfare costs of in‡ation were around 10% of GDP in terms of the consumption goods foregone. The big di¤erence between our results and Lucas (2000) are mainly due to the Harberger triangle in the market for loans (inside money) which makes part of the household transaction time, of the …rm ‡oat labor and of the distortion in the banking industry. This triangle arises due to the widening interest rates spread in the presence of a distorting inflation tax and under a fractionally reserve system. The Harberger triangle can represent 80% of the total welfare costs of inflation while the remaining percentage is split almost equally between the Bailey triangle and the resources used for the bank services. Finally, we formulate several theorems in terms of the optimal nonneutral monetary policy so as to compare with the classical monetary theory.

Do equity and foreign currency risk premiums display common patterns?

In da Costa et al. (2006) we have shown how a same pricing kernel can account for the excess returns of the S&:P500 over the US short term bond and of the uncovered over the covered trading of foreign government bonds. In this paper we estimate and test the overidentifying restrictiom; of Euler equations associated with "ix different versions of the Consumption Capital Asset Pricing I\Iodel. Our main finding is that the same (however often unreasonable) values for the parameters are estimated for ali models in both nmrkets. In most cases, the rejections or otherwise of overidentifying restrictions occurs for the two markets, suggesting that success and failure stories for the equity premium repeat themselves in foreign exchange markets. Our results corroborate the findings in da Costa et al. (2006) that indicate a strong similarity between the behavior of excess returns in the two markets when modeled as risk premiums, providing empirical grounds to believe that the proposed preference-based solutions to puzzles in domestic financiaI markets can certainly shed light on the Forward Premium Puzzle.

Geographical externalities and the Pacifying Police Units Program in Rio de Janeiro

The paper quantifies the effects on violence and police activity of the Pacifying Police Unit program (UPP) in Rio de Janeiro and the possible geographical spillovers caused by this policy. This program consists of taking selected shantytowns controlled by criminals organizations back to the State. The strategy of the policy is to dislodge the criminals and then settle a permanent community-oriented police station in the slum. The installation of police units in these slums can generate geographical spillover effects to other regions of the State of Rio de Janeiro. We use the interrupted time series approach proposed by Gonzalez-Navarro (2013) to address effects of a police when there is contagion of the control group and we find that criminal outcomes decrease in areas of UPP and in areas near treated regions. Furthermore, we build a model which allows to perform counterfactuals of this policy and to estimate causal effects in other areas of the State of Rio de Janeiro outside the city.

International portfolio diversification: evidence from emerging markets

Taking into account previous research we could assume to be beneficial to diversify investments in emerging economies. We investigate in the paper International Portfolio Diversification: evidence from Emerging Markets if it still holds true, given the assumption of larger world markets integration. Our results suggest a wide spread positive time-varying correlations of emerging and developed markets. However, pair-wise cross-country correlations gave evidence that emerging markets have low integration with developed markets. Consequently, we evaluate out-of-sample performance of a portfolio with emerging equity countries, confirming the initial statement that it has a better a risk-adjusted performance over a purely developed markets portfolio.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Avaliação espectrofotométrica do manchamento de resinas odontológicas contendo nanopartículas

Composite resins have been subjected to structural modifications aiming at improved optical and mechanical properties. The present study consisted in an in vitro evaluation of the staining behavior of two nanohybrid resins (NH1 and NH2), a nanoparticulated resin (NP) and a microhybrid resin (MH). Samples of these materials were prepared and immersed in commonly ingested drinks, i.e., coffee, red wine and acai berry for periods of time varying from 1 to 60 days. Cylindrical samples of each resin were shaped using a metallic die and polymerized during 30 s both on the bottom and top of its disk. All samples were polished and immersed in the staining solutions. After 24 hours, three samples of each resin immersed in each solution were removed and placed in a spectrofotome ter for analysis. To that end, the samples were previously diluted in HCl at 50%. Tukey tests were carried out in the statistical analysis of the results. The results revealed that there was a clear difference in the staining behavior of each material. The nanoparticulated resin did not show better color stability compared to the microhybrid resin. Moreover, all resins stained with time. The degree of staining decreased in the sequence nanoparticulated, microhybrid, nanohybrid MH2 and MH1. Wine was the most aggressive drink followed by coffee and acai berry. SEM and image analysis revealed significant porosity on the surface of MH resin and relatively large pores on a NP sample. The NH2 resin was characterized by homogeneous dispersion of particles and limited porosity. Finally, the NH1 resin depicted the lowest porosity level. The results revealed that staining is likely related to the concentration of inorganic pa rticles and surface porosity

Simulação do escoamento monofásico em um estágio de uma bomba centrífuga utilizando técnicas de fluidodinâmica computacional

Oil production and exploration techniques have evolved in the last decades in order to increase fluid flows and optimize how the required equipment are used. The base functioning of Electric Submersible Pumping (ESP) lift method is the use of an electric downhole motor to move a centrifugal pump and transport the fluids to the surface. The Electric Submersible Pumping is an option that has been gaining ground among the methods of Artificial Lift due to the ability to handle a large flow of liquid in onshore and offshore environments. The performance of a well equipped with ESP systems is intrinsically related to the centrifugal pump operation. It is the pump that has the function to turn the motor power into Head. In this present work, a computer model to analyze the three-dimensional flow in a centrifugal pump used in Electric Submersible Pumping has been developed. Through the commercial program, ANSYS® CFX®, initially using water as fluid flow, the geometry and simulation parameters have been defined in order to obtain an approximation of what occurs inside the channels of the impeller and diffuser pump in terms of flow. Three different geometry conditions were initially tested to determine which is most suitable to solving the problem. After choosing the most appropriate geometry, three mesh conditions were analyzed and the obtained values were compared to the experimental characteristic curve of Head provided by the manufacturer. The results have approached the experimental curve, the simulation time and the model convergence were satisfactory if it is considered that the studied problem involves numerical analysis. After the tests with water, oil was used in the simulations. The results were compared to a methodology used in the petroleum industry to correct viscosity. In general, for models with water and oil, the results with single-phase fluids were coherent with the experimental curves and, through three-dimensional computer models, they are a preliminary evaluation for the analysis of the two-phase flow inside the channels of centrifugal pump used in ESP systems

Metodologia para avaliação de cinemática de partículas gasosas em fluidos de viscosidade variável com o tempo e sua aplicação na construção de poços de petróleo

Many challenges have been presented in petroleum industry. One of them is the preventing of fluids influx during drilling and cementing. Gas migration can occur as result of pressure imbalance inside the well when well pressure becomes lower than gas zone pressure and in cementing operation this occurs during cement slurry transition period (solid to fluid). In this work it was developed a methodology to evaluate gas migration during drilling and cementing operations. It was considered gel strength concept and through experimental tests determined gas migration initial time. A mechanistic model was developed to obtain equation that evaluates bubble displacement through the fluid while it gels. Being a time-dependant behavior, dynamic rheological measurements were made to evaluate viscosity along the time. For drilling fluids analyzed it was verified that it is desirable fast and non-progressive gelation in order to reduce gas migration without affect operational window (difference between pore and fracture pressure). For cement slurries analyzed, the most appropriate is that remains fluid for more time below critical gel strength, maintaining hydrostatic pressure above gas zone pressure, and after that gels quickly, reducing gas migration. The model developed simulates previously operational conditions and allow changes in operational and fluids design to obtain a safer condition for well construction

Estudo da topologia de redes de conexão funcional no córtex sensorial primário e hipocampo durante o sono de ondas lentas

Complex network analysis is a powerful tool into research of complex systems like brain networks. This work aims to describe the topological changes in neural functional connectivity networks of neocortex and hippocampus during slow-wave sleep (SWS) in animals submited to a novel experience exposure. Slow-wave sleep is an important sleep stage where occurs reverberations of electrical activities patterns of wakeness, playing a fundamental role in memory consolidation. Although its importance there s a lack of studies that characterize the topological dynamical of functional connectivity networks during that sleep stage. There s no studies that describe the topological modifications that novel exposure leads to this networks. We have observed that several topological properties have been modified after novel exposure and this modification remains for a long time. Major part of this changes in topological properties by novel exposure are related to fault tolerance

Desenvolvimento de um Sistema de Visão Global para uma Frota de Mini-Robôs Móveis

Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second