875 resultados para out-of-sample forecast


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Using information on US domestic financial data only, we build a stochastic discount factor—SDF— and check whether it accounts for foreign markets stylized facts that escape consumption based models. By interpreting our SDF as the projection of a pricing kernel from a fully specified model in the space of returns, our results indicate that a model that accounts for the behavior of domestic assets goes a long way toward accounting for the behavior of foreign assets prices. We address predictability issues associated with the forward premium puzzle by: i) using instruments that are known to forecast excess returns in the moments restrictions associated with Euler equations, and; ii) by comparing this out-of-sample results with the one obtained performing an in-sample exercise, where the return-based SDF captures sources of risk of a representative set of developed and emerging economies government bonds. Our results indicate that the relevant state variables that explain foreign-currency market asset prices are also the driving forces behind U.S. domestic assets behavior.

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This paper constructs an indicator of Brazilian GDP at the monthly ftequency. The peculiar instability and abrupt changes of regimes in the dynamic behavior of the Brazilian business cycle were explicitly modeled within nonlinear ftameworks. In particular, a Markov switching dynarnic factor model was used to combine several macroeconomic variables that display simultaneous comovements with aggregate economic activity. The model generates as output a monthly indicator of the Brazilian GDP and real time probabilities of the current phase of the Brazilian business cycle. The monthly indicator shows a remarkable historical conformity with cyclical movements of GDP. In addition, the estimated filtered probabilities predict ali recessions in sample and out-of-sample. The ability of the indicator in linear forecasting growth rates of GDP is also examined. The estimated indicator displays a better in-sample and out-of-sample predictive performance in forecasting growth rates of real GDP, compared to a linear autoregressive model for GDP. These results suggest that the estimated monthly indicator can be used to forecast GDP and to monitor the state of the Brazilian economy in real time.

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R.R.M. de Sousa et al. Nitriding in cathodic cage of stainless steel AISI 316: Influence of sample position. Vacuum, [s.l.], n.83, 2009. Disponivel em: . Acesso em: 04 out.2010.

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Objectives: This study compared three methods of Streptococcus mutans and Lactobacillus spp. detection in the oral cavity: saliva swab (SS)-sample of stimulated saliva collected with swab; whole saliva (WS)-sample of 2 ml of stimulated saliva; and the dental plaque method (DP)-plaque sample of all dental surfaces.Methods: Thirty children were included in this study. In the first 15 children, the SS and WS methods were carried out before the dental plaque collection, and in the following 15, the sequence was inverted to evaluate possible interference of the methods sequence. The samples were diluted and inoculated in SB20 and Rogosa agar, respectively for S. mutans and Lactobacillus spp., at 37 degrees C for 48 h.Results: the results (cfu/mL) of S. mutans were analysed by the statistical Friedman's test. The levels of Lactobacillus spp. were analysed by descriptive statistics due to the high proportion of zero counts in the culture. In the first sequence of methods, the number of S. mutans counted for the SS method was inferior to DP and WS (P < 0.05), and the results for the WS and DP methods were similar. The detection of Lactobacillus spp. was observed just by the WS (100 %) and SS (14.3 %) methods. However, in the second experimental set the number of S. mutans detected by the DP method was similar to those of the SS and WS, however, the WS method showed higher values than SS (P < 0.05). A greater number of Lactobacillus spp. was detected by the WS method (100 %), followed by SS (55.5 %) and DP (33.3 %).Conclusions: the dental plaque collection and the sample of stimulated whole saliva presented similar results in the S. mutans count. The most suitable method to detect the Lactobacillus spp. level in the oral cavity is the stimulated whole saliva method. (c) 2004 Elsevier Ltd. All rights reserved.

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A new method was developed for the simultaneous determination of As, Bi, Sb, and Se by flow injection hydride generation graphite furnace atomic absorption spectrometry. An alternative two-step sample treatment procedure was used. The sample was heated (80degreesC) for 10 min in 6 M HCl to reduce Se(VI) to Se(IV), followed by the addition of 1% (m/v) thiourea solution to reduce arsenic and antimony from the pentavalent to the trivalent states.With this procedure, all analytes were converted to their most favorable and sensitive oxidation states to generate the corresponding hydrides. The pre-treated sample solution was then processed in the flow system for in situ trapping and atomization in a graphite tube coated with iridium. The impermanent modifier remained stable up to 300 firings and new coating out significant were possible wit changes in the analytical performance.The accuracy was checked for As, Bi, Sb, and Se determination in water standard reference materials NIST 1640 and 1643d and the results were in agreement with the certified values at a 95% confidence level. Good recoveries (94-104%.) of spiked mineral waters and synthetic As(V), Sb(Ill), mixtures of As(Ill), Sb(V), Se(VI), and Se(IV) were also found. Calculated characteristic masses were 32 mug As, 79 mug Bi, 35 mug Sb, and 130 pg Se, and the corresponding limits of detection were 0.06, 0.16, 0.19, and 0.59 mug L-1, respectively. The repeatability for a typical solution containing 5 mug L-1 As, Bi, Sb, and Se was in the 1-3% range.

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OBJECTIVE: To develop and evaluate the psychometric properties of a measure of motivation and life outlook (Getting-Out-of-Bed [GoB]). DESIGN: Secondary analysis of baseline and 6-month data from a longitudinal follow-up study of older breast cancer survivors. PARTICIPANTS: Women (N = 660) diagnosed with primary breast cancer stage I-IIIA disease, age >or=65 years, and permission to contact from an attending physician in four geographic regions in the United States (city-based Los Angeles, California; statewide in Minnesota, North Carolina, and Rhode Island). MEASUREMENT: Data were collected over 6-months of follow-up from consenting patients' medical records and telephone interviews with patients. Data collected included the 4-item GoB, health-related quality of life (HRQoL), breast cancer, sociodemographic, and health-related characteristics. RESULTS: Factor analysis produced, as hypothesized, one principal component with eigen values of 2.74(baseline) and 2.91(6-months) which explained 68.6%(baseline) and 72.7%(6-months) of total variance. In further psychometric analyses, GoB exhibited good construct validity (divergent: low nonstatistically significant correlations with unrelated constructs; convergent: moderate statistically significant correlations with related constructs; discriminant: distinguished high HRQoL groups with a high level of significance), excellent internal reliability (Cronbach's alpha 0.84(baseline), 0.87(6-months)), and produced stable measurements over 6-months. Women with GoB scores >or=50 at baseline were more likely at 6-months to have good HRQoL, good self-perceived health, and report regular exercise, indicating good predictive ability. CONCLUSION: GoB demonstrated overall good psychometric properties in this sample of older breast cancer survivors, suggestive of a promising tool for assessing motivation and life outlook in older adults. Nevertheless, because it was developed and initially evaluated in a select sample, using measures with similar but not exact content overlap further evaluation is needed before it can be recommended for widespread use.

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There is currently much interest in the appropriate use of obstetrical technology, cost containment and meeting consumers' needs for safe and satisfying maternity care. At the same time, there has been an increase in professionally unattended home births. In response, a new type of service, the out-of-hospital childbearing center (CBC) has been developed which is administratively and structurally separate from the hospital. In the CBC, maternity care is provided by certified nurse-midwives to carefully screened low risk childbearing families in conjunction with physician and hospital back-up.^ It was the purpose of this study to accomplish the following objectives: (1) To describe in a historical prospective study the demographic and medical-obstetric characteristics of patients laboring in eleven selected out-of-hospital childbearing centers in the United States from May 1, 1972, to December 15, 1979. Labor is defined as the onset of regular contractions as determined by the patient. (2) To describe any differences between those patients who require transfer to a back-up hospital and those who do not. (3) To describe administrative and service characteristics of eleven selected out-of-hospital childbearing centers in the United States. (4) To compare the demographic and medical-obstetric characteristics of women laboring in eleven selected out-of-hospital childbearing centers with a national sample of women of similar obstetric risk who according to birth certificates delivered legitimate infants in a hospital setting in the United States in 1972.^ Research concerning CBCs and supportive to the development of CBCs including studies which identified factors associated with fetal and perinatal morbidity and mortality, obstetrical risk screening, and the progress of technological development in obstetrics were reviewed. Information concerning the organization and delivery of care at each selected CBC was also collected and analyzed.^ A stratified, systematic sample of 1938 low risk women who began labor in a selected CBC were included in the study. These women were not unlike those described previously in small single center studies reported in the literature. The mean age was 25 years. Sixty-three per cent were white, 34 per cent Hispanic, 88 per cent married, 45 per cent had completed at least two years of college, nearly one-third were professionals and over a third were housewives. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of school.) UMI ^

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This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.

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The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011

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Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.

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The cortisol awakening response (CAR) is typically measured in the domestic setting. Moderate sample timing inaccuracy has been shown to result in erroneous CAR estimates and such inaccuracy has been shown partially to explain inconsistency in the CAR literature. The need for more reliable measurement of the CAR has recently been highlighted in expert consensus guidelines where it was pointed out that less than 6% of published studies provided electronic-monitoring of saliva sampling time in the post-awakening period. Analyses of a merged data-set of published studies from our laboratory are presented. To qualify for selection, both time of awakening and collection of the first sample must have been verified by electronic-monitoring and sampling commenced within 15 min of awakening. Participants (n = 128) were young (median age of 20 years) and healthy. Cortisol values were determined in the 45 min post-awakening period on 215 sampling days. On 127 days, delay between verified awakening and collection of the first sample was less than 3 min (‘no delay’ group); on 45 days there was a delay of 4–6 min (‘short delay’ group); on 43 days the delay was 7–15 min (‘moderate delay’ group). Cortisol values for verified sampling times accurately mapped on to the typical post-awakening cortisol growth curve, regardless of whether sampling deviated from desired protocol timings. This provides support for incorporating rather than excluding delayed data (up to 15 min) in CAR analyses. For this population the fitted cortisol growth curve equation predicted a mean cortisol awakening level of 6 nmols/l (±1 for 95% CI) and a mean CAR rise of 6 nmols/l (±2 for 95% CI). We also modelled the relationship between real delay and CAR magnitude, when the CAR is calculated erroneously by incorrectly assuming adherence to protocol time. Findings supported a curvilinear hypothesis in relation to effects of sample delay on the CAR. Short delays of 4–6 min between awakening and commencement of saliva sampling resulted an overestimated CAR. Moderate delays of 7–15 min were associated with an underestimated CAR. Findings emphasize the need to employ electronic-monitoring of sampling accuracy when measuring the CAR in the domestic setting.

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R.R.M. de Sousa et al. Nitriding in cathodic cage of stainless steel AISI 316: Influence of sample position. Vacuum, [s.l.], n.83, 2009. Disponivel em: . Acesso em: 04 out.2010.

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Thesis (Master's)--University of Washington, 2016-06

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R.R.M. de Sousa et al. Nitriding in cathodic cage of stainless steel AISI 316: Influence of sample position. Vacuum, [s.l.], n.83, 2009. Disponivel em: . Acesso em: 04 out.2010.

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Objective: To assess the quality of the labels for clinical trial samples through current regulations, and to analyze its potential correlation with the specific characteristics of each sample. Method: A transversal multicenter study where the clinical trial samples from two third level hospitals were analyzed. The eleven items from Directive 2003/94/EC, as well as the name of the clinical trial and the dose on the label cover, were considered variables for labelling quality. The influence of the characteristics of each sample on labelling quality was also analyzed. Outcome: The study included 503 samples from 220 clinical trials. The mean quality of labelling, understood as the proportion of items from Appendix 13, was of 91.9%. Out of these, 6.6% did not include the name of the sample in the outer face of the label, while in 9.7% the dose was missing. The samples with clinical trial-type samples presented a higher quality (p < 0.049), blinding reduced their quality (p = 0.017), and identification by kit number or by patient increased it (p < 0.01). The promoter was the variable which introduced the highest variability into the analysis. Conclusions: The mean quality of labelling is adequate in the majority of clinical trial samples. The lack of essential information in some samples, such as the clinical trial code and the period of validity, is alarming and might be the potential source for dispensing or administration errors.