949 resultados para Stochastic covariate
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OBJECTIVE: Studies conducted mainly in countries located in the Northern Hemisphere have shown that season of birth influences mood seasonality. Greater mood seasonality has been observed for individuals born during spring/summer months than those born during autumn/winter months. Expanding past research to the Southern Hemisphere, in this study we examine the influence of season of birth on mood seasonality in a sample of 1,247 healthy young Brazilians. METHOD: The Seasonal Pattern Assessment Questionnaire was used to compute a global seasonality score as a measure of mood seasonality in a cross-sectional study. RESULTS: Analysis of covariance was conducted to examine the effects of month of birth and gender on mood seasonality, with age entered as a covariate. A main effect of gender was observed, F (1, 1197) = 17.86, p < .01; partial Eta-squared = .02, with mood seasonality being higher for females (M = 8) than for males (M = 7). Contradicting previous findings, no significant main effect for month of birth was observed, F (1, 1197) = 0.65, p > .05. CONCLUSION: The unexpected finding is tentatively explained by differences in geographic location and weather fluctuations between the sampling location in Brazil and other countries where season of birth has been found to influence mood seasonality. Additional studies with larger samples from the Southern Hemisphere are necessary to shed additional light on the possible significant influence of season of birth on mood.
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First published online: December 16, 2014.
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Simulation, modelling, proxels, PDEs, Markov chains, Petri nets, stochastic, performability, transient analysis
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Fluidized beds, granulation, heat and mass transfer, calcium dynamics, stochastic process, finite element methods, Rosenbrock methods, multigrid methods, parallelization
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The golden mussel, Limnoperna fortunei (Dunker, 1857), has been found in the estuarine regions of South America, including the Patos Lagoon (Brazil), a huge choked lagoon with an estuarine region that is highly unstable chemically. Limnoperna fortunei space-temporal variability in the lagoon's estuarine region demonstrated the need to evaluate this species' ability to survive under salinity shocks. A set of experiments was conducted under controlled laboratory conditions. Specimens were tested under salinities of 2, 4, 6, 8 and 12 ppt, and were exposed for periods of 24, 48, 72, 96 and 240 hours. The mussel can survive (90%) up to a salinity shock of 2 ppt for periods of at least 10 days. Considering the influence of climatic and stochastic events and the chemical instability of the Patos Lagoon estuarine region, it's unlikely that populations could survive for longer periods (more than a year) in this area.
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The zooplankton community presents stochastic temporal fluctuation and heterogeneous spatial variation determined by the relationships among the organisms and environmental conditions. We predicted that the temporal and spatial zooplankton distribution is heterogeneous and discrete, respectively, and that the daily variation of most abundant species is related to environmental conditions, specifically the availability of resources. Zooplankton samples were collected daily at three sampling stations in a lateral arm of the Rosana Reservoir (SP/PR). The zooplankton did not present significant differences in abundance and evenness among sampling stations, but the temporal variation of these attributes was significant. Abiotic variables and algal resource availability have significantly explained the daily variation of the most abundant species (p<0.001), however, the species distribution makes inferences on biotic relationships between them. Thus, not only the food resource availability is influential on the abundance of principal zooplankton species, but rather a set of factors (abiotic variables and biotic relationships).
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We quantify the long-time behavior of a system of (partially) inelastic particles in a stochastic thermostat by means of the contractivity of a suitable metric in the set of probability measures. Existence, uniqueness, boundedness of moments and regularity of a steady state are derived from this basic property. The solutions of the kinetic model are proved to converge exponentially as t→ ∞ to this diffusive equilibrium in this distance metrizing the weak convergence of measures. Then, we prove a uniform bound in time on Sobolev norms of the solution, provided the initial data has a finite norm in the corresponding Sobolev space. These results are then combined, using interpolation inequalities, to obtain exponential convergence to the diffusive equilibrium in the strong L¹-norm, as well as various Sobolev norms.
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This paper assesses empirically the importance of size discrimination and disaggregate data for deciding where to locate a start-up concern. We compare three econometric specifications using Catalan data: a multinomial logit with 4 and 41 alternatives (provinces and comarques, respectively) in which firm size is the main covariate; a conditional logit with 4 and 41 alternatives including attributes of the sites as well as size-site interactions; and a Poisson model on the comarques and the full spatial choice set (942 municipalities) with site-specific variables. Our results suggest that if these two issues are ignored, conclusions may be misleading. We provide evidence that large and small firms behave differently and conclude that Catalan firms tend to choose between comarques rather than between municipalities. Moreover, labour-intensive firms seem more likely to be located in the city of Barcelona. Keywords: Catalonia, industrial location, multinomial response model. JEL: C250, E30, R00, R12
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We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.
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This paper provides empirical evidence that continuous time models with one factor of volatility, in some conditions, are able to fit the main characteristics of financial data. It also reports the importance of the feedback factor in capturing the strong volatility clustering of data, caused by a possible change in the pattern of volatility in the last part of the sample. We use the Efficient Method of Moments (EMM) by Gallant and Tauchen (1996) to estimate logarithmic models with one and two stochastic volatility factors (with and without feedback) and to select among them.
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This paper evaluates the forecasting performance of a continuous stochastic volatility model with two factors of volatility (SV2F) and compares it to those of GARCH and ARFIMA models. The empirical results show that the volatility forecasting ability of the SV2F model is better than that of the GARCH and ARFIMA models, especially when volatility seems to change pattern. We use ex-post volatility as a proxy of the realized volatility obtained from intraday data and the forecasts from the SV2F are calculated using the reprojection technique proposed by Gallant and Tauchen (1998).
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From the classical gold standard up to the current ERM2 arrangement of the European Union, target zones have been a widely used exchange regime in contemporary history. This paper presents a benchmark model that rationalizes the choice of target zones over the rest of regimes: the fixed rate, the free float and the managed float. It is shown that the monetary authority may gain efficiency by reducing volatility of both the exchange rate and the interest rate at the same time. Furthermore, the model is consistent with some known stylized facts in the empirical literature that previous models were not able to produce, namely, the positive relation between the exchange rate and the interest rate differential, the degree of non-linearity of the function linking the exchage rate to fundamentals and the shape of the exchange rate stochastic distribution.
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This paper investigates the role of variable capacity utilization as a source of asymmetries in the relationship between monetary policy and economic activity within a dynamic stochastic general equilibrium framework. The source of the asymmetry is directly linked to the bottlenecks and stock-outs that emerge from the existence of capacity constraints in the real side of the economy. Money has real effects due to the presence of rigidities in households' portfolio decisions in the form of a Luces-Fuerst 'limited participation' constraint. The model features variable capacity utilization rates across firms due to demand uncertainty. A monopolistic competitive structure provides additional effects through optimal mark-up changes. The overall message of this paper for monetary policy is that the same actions may have different effects depending on the capacity utilization rate of the economy.
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We consider multidimensional backward stochastic differential equations (BSDEs). We prove the existence and uniqueness of solutions when the coefficient grow super-linearly, and moreover, can be neither locally Lipschitz in the variable y nor in the variable z. This is done with super-linear growth coefficient and a p-integrable terminal condition (p & 1). As application, we establish the existence and uniqueness of solutions to degenerate semilinear PDEs with superlinear growth generator and an Lp-terminal data, p & 1. Our result cover, for instance, the case of PDEs with logarithmic nonlinearities.
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In this paper we prove that the solution of a backward stochastic differential equation, which involves a subdifferential operator and associated to a family of reflecting diffusion processes, converges to the solution of a deterministic backward equation and satisfes a large deviation principle.