933 resultados para Uncertainty in Wind Energy


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We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

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We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

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We analyse the role of time-variation in coefficients and other sources of uncertainty in exchange rate forecasting regressions. Our techniques incorporate the notion that the relevant set of predictors and their corresponding weights, change over time. We find that predictive models which allow for sudden rather than smooth, changes in coefficients significantly beat the random walk benchmark in out-of-sample forecasting exercise. Using innovative variance decomposition scheme, we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients' variability, as the main factors hindering models' forecasting performance. The uncertainty regarding the choice of the predictor is small.

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BACKGROUND/OBJECTIVES: To measure resting metabolic rate (RMR), activity energy expenditure (AEE), total energy expenditure (TEE) and physical activity pattern, that is, duration and intensity (in metabolic equivalents, METs) of activities performed in late pregnancy compared with postpartum in healthy, well-nourished women living in Switzerland. SUBJECTS/METHODS: Weight, height, RMR, AEE, TEE and physical activity patterns were measured longitudinally in 27 healthy women aged 23-40 years at 38.2+/-1.5 weeks of gestation and 40.0+/-7.2 weeks postpartum. RESULTS: The RMR during late pregnancy was 7480 kJ per day, that is, 1320+/-760 kJ per day (21.4%) higher than the postpartum RMR (P<0.001). Absolute changes in RMR were positively correlated with the corresponding changes in body weight (r=0.61, P<0.001). RMR per kg body weight was similar in late pregnancy vs postpartum (P=0.28). AEE per kg during pregnancy and postpartum was 40+/-13 and 50+/-20 kJ/kg, respectively (P=0.001). There were significant differences in daily time spent at METs<1.5 (1067 vs 998 min, P=0.045), at 2.5< or =METs <3.0 (58 vs 82 min, P=0.002) and METs> or =6 (1 vs 6 min, P=0.014) during pregnancy and postpartum, respectively. CONCLUSIONS: Energy expenditure in healthy women living in Switzerland increases in pregnancy compared with the postpartum state. Additional energy expenditure is primarily attributed to an increase in RMR, which is partly compensated by a decrease in AEE. The decrease in physical activity-related energy costs is achieved by selecting less demanding activities and should be taken into account when defining extra energy requirements for late pregnancy in Switzerland.

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1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

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This paper study repeated games where the time repetitions of the stage game are not known or controlled by the players. We call this feature random monitoring. Kawamori's (2004) shows that perfect random monitoring is always better than the canonical case. Surprisingly, when the monitoring is public, the result is less clear-cut and does not generalize in a straightforward way. Unless the public signals are sufficiently informative about player's actions and/or players are patient enough. In addition to a discount effect, that tends to consistently favor the provision of incentives, we found an information effect, associated with the time uncertainty on the distribution of public signals. Whether payoff improvements are or not possible, depends crucially on the direction and strength of these effects. JEL: C73, D82, D86. KEYWORDS: Repeated Games, Frequent Monitoring, Random Public Monitoring, Moral Hazard, Stochastic Processes.

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El novembre de 2010, la Comissió Europea ha finalment donat a conèixer la seva "Energia 2020 Comunicació", un document estratègic en el marc més ampli del programa "Europa 2020". Una estratègia per al desenvolupament sostenible intel · ligent, i creixement inclusiu posa les bases d'un nou enfocament a la política d'energia a la UE. En el marc d'Europa 2020, la Iniciativa d'Energia recopila els resultats que ja s'han obtingut a través de la Estratègia de Lisboa 2000-2010, s'identifiquen les deficiències del passat i i introdueix nous objectius ambiciosos per a la UE en matèria de política energètica.

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We compare behavior in modified dictator games with and without role uncertainty. Subjectschoose between a selfish action, a costly surplus creating action (altruistic behavior) and acostly surplus destroying action (spiteful behavior). While costly surplus creating actions are themost frequent under role uncertainty (64%), selfish actions become the most frequent withoutrole uncertainty (69%). Also, the frequency of surplus destroying choices is negligible with roleuncertainty (1%) but not so without it (11%). A classification of subjects into four differenttypes of interdependent preferences (Selfish, Social Welfare maximizing, Inequity Averse andCompetitive) shows that the use of role uncertainty overestimates the prevalence of SocialWelfare maximizing preferences in the subject population (from 74% with role uncertainty to21% without it) and underestimates Selfish and Inequity Averse preferences. An additionaltreatment, in which subjects undertake an understanding test before participating in theexperiment with role uncertainty, shows that the vast majority of subjects (93%) correctlyunderstand the payoff mechanism with role uncertainty, but yet surplus creating actions weremost frequent. Our results warn against the use of role uncertainty in experiments that aim tomeasure the prevalence of interdependent preferences.

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This paper explores three aspects of strategic uncertainty: its relation to risk, predictability of behavior and subjective beliefs of players. In a laboratory experiment we measure subjects certainty equivalents for three coordination games and one lottery. Behavior in coordination games is related to risk aversion, experience seeking, and age.From the distribution of certainty equivalents we estimate probabilities for successful coordination in a wide range of games. For many games, success of coordination is predictable with a reasonable error rate. The best response to observed behavior is close to the global-game solution. Comparing choices in coordination games with revealed risk aversion, we estimate subjective probabilities for successful coordination. In games with a low coordination requirement, most subjects underestimate the probability of success. In games with a high coordination requirement, most subjects overestimate this probability. Estimating probabilistic decision models, we show that the quality of predictions can be improved when individual characteristics are taken into account. Subjects behavior is consistent with probabilistic beliefs about the aggregate outcome, but inconsistent with probabilistic beliefs about individual behavior.

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An in situ energy budget of the hydropolyp Eudendrium racemosum (Cavolini, 1785) is presented. Ingestion and respiration rates and ammonium excretion were studied over two 24 h cycles, with two-hour sample intervals. The species ingested as much as 25.9% of its own biomass per day (minimum rate). Respiration was 1.62 ml O2 g-1 d w h-1 while excretion was 13.6 mM NH4 g-1dw h-1. We estimated that the species increased its biomass at a rate of 9.6% per day (Growth + Reproduction). This value is higher than those previously reported for other cnidarians. We can assume that the capacity of E. racemosum to survive - albeit for a limited period of the year - in the highly-competitive shallow-water communities is based on its high growth rate.

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Energy demand is an important constraint on neural signaling. Several methods have been proposed to assess the energy budget of the brain based on a bottom-up approach in which the energy demand of individual biophysical processes are first estimated independently and then summed up to compute the brain's total energy budget. Here, we address this question using a novel approach that makes use of published datasets that reported average cerebral glucose and oxygen utilization in humans and rodents during different activation states. Our approach allows us (1) to decipher neuron-glia compartmentalization in energy metabolism and (2) to compute a precise state-dependent energy budget for the brain. Under the assumption that the fraction of energy used for signaling is proportional to the cycling of neurotransmitters, we find that in the activated state, most of the energy ( approximately 80%) is oxidatively produced and consumed by neurons to support neuron-to-neuron signaling. Glial cells, while only contributing for a small fraction to energy production ( approximately 6%), actually take up a significant fraction of glucose (50% or more) from the blood and provide neurons with glucose-derived energy substrates. Our results suggest that glycolysis occurs for a significant part in astrocytes whereas most of the oxygen is utilized in neurons. As a consequence, a transfer of glucose-derived metabolites from glial cells to neurons has to take place. Furthermore, we find that the amplitude of this transfer is correlated to (1) the activity level of the brain; the larger the activity, the more metabolites are shuttled from glia to neurons and (2) the oxidative activity in astrocytes; with higher glial pyruvate metabolism, less metabolites are shuttled from glia to neurons. While some of the details of a bottom-up biophysical approach have to be simplified, our method allows for a straightforward assessment of the brain's energy budget from macroscopic measurements with minimal underlying assumptions.

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Työn päätavoite on selvittää kuinka erityisesti sähkön markkinahinnan ennustamiseen ja johdannaismarkkinoiden tietämykseen perustuva lyhyen tähtäimen sähköjohdannaisten hyödyntäminen tapahtuu teollisessa energianhallinnassa. Tätä aihetta lähestytään luomalla prosessi lyhyen tähtäimen sähköjohdannaisten hyödyntämiselle. Prosessi esitellään ja selvitetään aina lähtökohdista todelliseen kaupankäyntiin asti erillisen esimerkkitehtaan avulla.Lyhyen tähtäimen sähköjohdannaisten hyödyntäminen teollisessa energianhallinnassa perustuu pääosin tulevaisuuden odotuksiin sähkön markkinahinnan kehittymisestä sekä tehtaiden operatiiviseen tilanteeseen. Operatiiviseen tilanteeseen perustuva lyhyen tähtäimen sähköjohdannaisten kaupankäynti on pääasiassa pitkän tähtäimen suojausten sopeuttamista lyhyelle tähtäimelle sopivaksi.Hinnan ennustamisella on suuri rooli lyhyen tähtäimen sähköjohdannaisten hyödyntämisprosessissa. Työssä esitelty hinnan ennustamismalli on sopiva päivä- ja viikkotason Nord Poolin Elspot -systeemihinnan ennustamiseen. Elspot -systeemihinnan ennustamismalli on suunniteltu käytännönläheiseksi ja sen perustana ovat todelliset fysikaaliset ja mitattavat suureet. Futuurimarkkinatietämys on tarpeen lyhyen tähtäimen johdannaisia käytettäessä. Työssä tutkitaan yleisiä markkinoiden odotuksia ja futuurimarkkinoiden tietoisuuden kehittymistä koskien tulevaa vallitsevaa tilannetta. Työssä luodaan myös työkalu, mikä auttaa kaupan laatijaa muodostamaan suuntaa-antavat todennäköisyydet eri hintanäkemyksille ja paikallistamaan mahdolliset markkinoiden epätodennäköiset hintaodotukset.Kokemukset Elspot -systeemihinnan ennustamismallin soveltamisesta ovat lupaavia. Lisäksi havainnot futuurimarkkinoiden käyttäytymisestä Nord Poolissa ja muodostettu työkalu suuntaa-antavien todennäköisyyksien selvittämiseksi auttavat kaupan laatijaa päätöksenteossa. Lyhyen tähtäimen sähköjohdannaisten hyödyntäminen teollisessa energianhallinnassa on periaatteessa mahdollista esitellyn prosessin avulla, vaikka täydellinen käyttöönotto vaatisi vielä joitakin järjestelyjä. Keskittymällä tilanteisiin jotka työssä kuvatulla prosessilla ovat hoidettavissa, työssä määritellyllä menettelyllä on mahdollisuudet saavuttaa epäedullisen hintakehityksen riskin väheneminen ja parempi taloudellinen tulos teollisen energianhallinnan sähkökaupankäynnissä.

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Euroopan energiamarkkinat ovat olleet viimeisen kymmenen vuoden aikana suurten muutosten alla. Markkinoiden kehitys on ollut huomattavaa myös Iso-Britanniassa, jossa sähkö- ja kaasumarkkinat ovat olleet avoinna kilpailulle jo muutamia vuosia. Ennen markkinoiden avautumista energiyhtiöt pystyivät siirtämään kaikki riskit suoraan asiakkaan kannettaviksi. Markkinoiden avautumisen myötä lisääntynyt kilpailu on kuitenkin pakottanut energiayhtiöitä ajanmukaistamaan näkemyksiään riskeistä. Riskitekijät, joista ei aiemmin tarvinnut välittää, on nyt pystyttävä tunnistamaan ja hallitsemaan. Tämä työ keskittyy hinta- ja volyymiriskien hallintaan. Rahoitusmarkkinoilla pitkään käytettyjä riskienhallintatyökaluja on otettu käyttöön myös energiamarkkinoilla. Energiamarkkinoiden piirteet poikkeavat kuitenkin rahoitusmarkkinoista, eikä näitä työkaluja voida ottaa käyttöön muutoksitta. Silti, jopa muutosten jälkeen rahoitusmarkkinoiden riskienhallitavälineet aliarvioivat energiamarkkinoiden hinta- ja volyymiriskejä. Tässä yhteydessä työssä esitetään Profit at Risk, PaR. PaR on skenaariopohjainen riskienhallinnan työkalu, joka on kehitetty erityisesti energiamarkkinoille ja täten huomioi niiden erikoispiirteet. Työn rungon muodostavat energiamarkkinoiden käyttäytyminen, hinta- ja volyymiriskitekijät sekä pohdinta miten hinta- ja volyymiriskeiltä voidaan suojautua ja miten niitä voidaan hallita. PaR-metodologiaa verrataan perinteisiin riskienhallintamenetelmiin ja työn tavoitteena on tuoda esiin ne tekijät, joiden ansiosta PaR on sopivampi työkalu energiamarkkinoiden riskienhallintaan kuin perinteiset menetelmät. Käytännön esimerkkinä työssä toimii Fortum Energy plus’n PaR –malli. Koska PaR on kehitetty erityisesti energiamarkkinoille, se huomioi täysin markkinoiden aiheuttamat hinta- ja volyymiriskit. Käytännön esimerkki kuitenkin osoittaa, että PaR menetelmästä ei ole riskienhallinnallista hyötyä ellei työkalun käyttäjällä ole täydellistä tietämystä niin energiamarkkinoista kuin markkinoiden muutoksiin vaikuttavien tekijöiden käyttäytymisestä.