太原市老年体育人口体质评价与分析

对2014年全国第四次国民体质监测太原监测点老年人体质的测试,运用文献资料、数理统计、测量法等方法,得出结论:老年体协人群中优秀率和良好率分别高于非老年体协人群,不合格率低于非老年体协人群;与非老年体协人群相比在身体形态上无显著差异;老年体协的健身活动对提高老年女性人群的身体柔韧性(坐位体前屈)效果明显,对提高老年人肺活量、神经系统反应能力(选择反应时)和握力均有显著效果,对平衡能力和控制力(闭眼单脚站立)的提高效果不明显。     

Diet composition of an invasive population of Lithobates catesbeianus (American Bullfrog) from Argentina

The American bullfrog Lithobates catesbeianus has been introduced around the world, with invasive populations reported from almost all South American countries. A population of this species was introduced in the Calingasta department of San Juan province, which is an arid environment in western Argentina. This work provides information on the dietary composition of an invasive population of L. catesbeianus, and compares the degree of dietary overlap between adults and juveniles. Stomach contents of 169 bullfrogs (82 adults and 87 juveniles) were analysed. Adults consumed 40 prey taxa and Hymenoptera (Insecta) was the most numerous prey item (41.8%), followed by Araneae (13.6%) and Aeglidae (13.4%). Juveniles consumed 29 prey taxa and Hymenoptera constituted the highest percentage in prey number (77.2%). The trophic overlap niche index at the same level shows a value of 0.64 overlap in dietary community between adults and juveniles of this bullfrog. Aeglidae was volumetrically the most important trophic item (25.4%), followed by Anura (25.02%). Our results showed that cannibalism in bullfrogs is more common than the consumption of native anurans, coinciding with that reported in other populations of introduced bullfrogs. The high similarity in the diets of both size classes and the association between the size of the predator and prey suggest that the impact caused by bullfrogs throughout their ontogeny is high and probably has an impact on their prey. Freshwater crabs are the main items in the diet of Lithobates catesbeianus in other introduced populations and are usually the most conspicuous at our study site. The crabs in freshwater ecosystems are part of the lowest trophic level in the food chain. The major threats to the southern region’s freshwater crabs include deforestation, farming and exotic species. Lithobates catesbeianus has a generalist diet and high overlap between adults and juveniles.     

Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts

By linking measures of forecast accuracy as well as testing procedures with regard to forecast rationality this paper investigates aggregated survey forecasts with forecast horizons of 3, 12, and 24 months for the exchange rates of the Chinese yuan, the Hong Kong dollar, the Japanese yen, and the Singapore dollar vis-à-vis the US dollar and, hence, for four different currency regimes. The rationality of the exchange rate predictions is initially assessed utilizing tests for unbiasedness and efficiency which indicate that the investigated forecasts are irrational in the sense that the predictions are biased. As one major contribution of this paper, it is subsequently shown that these results are not consistent with an alternative, less restrictive, measure of rationality. Investigating the order of integration of the time series as well as cointegrating relationships, this empirical evidence supports the conclusion that the majority of forecasts are in fact rational. Regarding forerunning properties of the predictions, the results are rather mediocre, with shorter term forecasts for the tightly managed USD/CNY FX regime being one exception. As one additional important and novel evaluation result, it can be concluded, that the currency regime matters for the quality of exchange rate forecasts.     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Correcting the January optimism effect

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak-form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

依赖尺度结构的竞争种群的最优出生率控制

讨论了一个具有尺度结构的竞争种群模型。首先利用特征线法以及Banach不动点定理对系统解的存在惟一性予以证明,通过比较原理证明系统解对控制变量的连续依赖性;最后利用法锥的定义,证明最优控制存在的必要性。     

美元/欧元汇率的趋势与波动分析及区间预测

对美元/欧元汇率进行趋势与波动分析并作出区间预测。利用BP神经网络提取趋势,对残差分别运用自回归移动平均模型和广义自回归条件异方差模型分析波动性,将趋势与波动性结合给出区间预测。对2001年7月至2017年10月美元/欧元汇率的研究发现,BP神经网络具有很好的非线性刻画能力,但只有合适的预测精度才能得出较好的预测区间,同时也发现,广义自回归条件异方差模型对波动性的分析效果优于自回归移动平均模型。因此,BP神经网络模型与广义自回归条件异方差模型的组合模型(BP-GARCH模型)更适合时间序列的中长期区间预测。通过调节BP神经网络的参数、误差及预测精度提高组合模型的精度。     

基于XGBoost的短时交通流预测模型

为提高路段短时交通流的预测精度,选取路段平均旅行时间作为预测指标,建立了一种基于极端样度上升(extrem gradient boosting,XGBoost)的短时交通流预测模型。首先通过对交通流数据的分析,在考虑交通流时空特性的基础上,分别构建目标路段时间序列训练集、测试集以及时空序列训练集、测试集,然后基于XGBoost模型以及构建的训练样本集建立时间序列预测模型以及时空序列预测模型,并利用训练好的模型进行预测,最后将模型预测结果与线性回归模型、神经网络模型预测结果进行比较。实验结果表明:基于XGBoost的短时交通流预测模型能够对路段未来时段平均旅行时间进行比较准确的预测,其中时间序列预测模型均方根误差为5. 32,时空序列预测模型均方根误差为4. 82,均低于线性回归模型和神经网络模型,且相比于仅考虑时间因素的短时交通流预测模型,同时考虑时空因素的预测模型得到的误差更低,预测效果更好。