192 resultados para 1099


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We evaluate the predictive power of leading indicators for output growth at horizons up to 1 year. We use the MIDAS regression approach as this allows us to combine multiple individual leading indicators in a parsimonious way and to directly exploit the information content of the monthly series to predict quarterly output growth. When we use real-time vintage data, the indicators are found to have significant predictive ability, and this is further enhanced by the use of monthly data on the quarter at the time the forecast is made

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We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

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Recent literature has suggested that macroeconomic forecasters may have asymmetric loss functions, and that there may be heterogeneity across forecasters in the degree to which they weigh under- and over-predictions. Using an individual-level analysis that exploits the Survey of Professional Forecasters respondents’ histogram forecasts, we find little evidence of asymmetric loss for the inflation forecasters

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This paper combines and generalizes a number of recent time series models of daily exchange rate series by using a SETAR model which also allows the variance equation of a GARCH specification for the error terms to be drawn from more than one regime. An application of the model to the French Franc/Deutschmark exchange rate demonstrates that out-of-sample forecasts for the exchange rate volatility are also improved when the restriction that the data it is drawn from a single regime is removed. This result highlights the importance of considering both types of regime shift (i.e. thresholds in variance as well as in mean) when analysing financial time series.

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This paper proposes and implements a new methodology for forecasting time series, based on bicorrelations and cross-bicorrelations. It is shown that the forecasting technique arises as a natural extension of, and as a complement to, existing univariate and multivariate non-linearity tests. The formulations are essentially modified autoregressive or vector autoregressive models respectively, which can be estimated using ordinary least squares. The techniques are applied to a set of high-frequency exchange rate returns, and their out-of-sample forecasting performance is compared to that of other time series models

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This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

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This paper forecasts Daily Sterling exchange rate returns using various naive, linear and non-linear univariate time-series models. The accuracy of the forecasts is evaluated using mean squared error and sign prediction criteria. These show only a very modest improvement over forecasts generated by a random walk model. The Pesaran–Timmerman test and a comparison with forecasts generated artificially shows that even the best models have no evidence of market timing ability.

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The majority of the UK population is either overweight or obese. Health economists, nutritionists and doctors are calling for the UK to follow the example of other European countries and introduce a tax on soft drinks as a result of the perception that high intakes contribute to diet-related disease. We use a demand model estimated with household-level data on beverage purchases in the UK to investigate the effects of a tax on soft drink consumption. The model is a Quadratic Almost Ideal Demand System, and censoring is handled by applying a double hurdle. Separate models are estimated for low, moderate and high consumers to allow for a differential impact on consumption between these groups. Applying different hypothetical tax rates, we conclude that understanding the nature of substitute/complement relationships is crucial in designing an effective policy as these relationships differ between consumers depending on their consumption level. The overall impact of a soft drink tax on calorie consumption is likely to be small.

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Brachyspira pilosicoli is a potentially zoonotic anaerobic intestinal spirochaete that is one of several species causing avian intestinal spirochaetosis. The aim of this study was to develop a reproducible model of infection in point-of-lay chickens and compare the virulence of two strains of B. pilosicoli in a model using experimentally challenged laying chickens. Seventeen-week-old commercial laying chickens were experimentally challenged by oral gavage with either B. pilosicoli strain B2904 or CPSp1, following an oral dose of 10 % sodium bicarbonate to neutralize acidity in the crop. Approximately 80 % of the chickens became colonized and exhibited increased faecal moisture content, reduced weight gain and delayed onset of lay. Tissues sampled at post-mortem examination were analysed to produce a quantitative output on the number of spirochaetes present and hence, the extent of colonization. The liver and spleen were colonized, and novel histopathology was observed in these tissues. The infection model we report here has potential use in studies to improve our understanding of the mechanisms by which Brachyspira elicit disease in poultry and in testing novel intervention strategies.

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Avian intestinal spirochaetosis (AIS) results from the colonization of the caeca and colon of poultry by pathogenic Brachyspira, notably Brachyspira pilosicoli. Following the ban on the use of antibiotic growth promoters in the European Union in 2006, the number of cases of AIS has increased, which, alongside emerging antimicrobial resistance in Brachyspira, has driven renewed interest in alternative intervention strategies. Lactobacillus-based probiotics have been shown to protect against infection with common enteric pathogens in livestock. Our previous studies have shown that Lactobacillus reuteri LM1 antagonizes aspects of the pathobiology of Brachyspira in vitro. Here, we showed that L. reuteri LM1 mitigates the clinical symptoms of AIS in chickens experimentally challenged with B. pilosicoli. Two groups of 15 commercial laying hens were challenged experimentally by oral gavage with B. pilosicoli B2904 at 18 weeks of age; one group received unsupplemented drinking water and the other received L. reuteri LM1 in drinking water from 1 week prior to challenge with Brachyspira and thereafter for the duration of the study. This treatment regime was protective. Specifically, B. pilosicoli was detected by culture in fewer birds, bird weights were higher, faecal moisture contents were significantly lower (P<0.05) and egg production as assessed by egg weight and faecal staining score was improved (P<0.05). Also, at post-mortem examination, significantly fewer B. pilosicoli were recovered from treated birds (P<0.05), with only mild–moderate histopathological changes observed. These data suggest that L. reuteri LM1 may be a useful tool in the control of AIS.

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almonella enterica serovar Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of Salmonella Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in energy demand, while growing in glucose minimal medium. By grouping reactions with similar flux responses, a sub-network of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions, that when removed from the genome-scale model interfered with energy and biomass generation. 11 such sets were found to be essential for the production of biomass precursors. Experimental investigation of 7 of these showed that knock-outs of the associated genes resulted in attenuated growth for 4 pairs of reactions, while 3 single reactions were shown to be essential for growth.

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This empirical study explores successful views and characteristics of leaders and employees in the SMEs of the People's Republic of China during the global financial crisis.