an trough weakens, the summer rain

in less precipitation in the Huaihe River valley. On the contrary, during negative index times, a weak summ     

Analysis and modelling of wind speed in New York

In this paper we propose an ARMA time-series model for the wind speed at a single spatial location, and estimate it on in-sample data recorded in three different wind farm regions in New York state. The data have a three-hour granularity, but based on applications to financial wind derivatives contracts, we also consider daily average wind speeds. We demonstrate that there are large discrepancies in the behaviour of daily average and three-hourly wind speed records. The validation procedure based on out-of-sample observations reflects that the proposed model is reliable and can be used for various practical applications, like, for instance, weather prediction, pricing of financial wind contracts, wind generated power, etc. Furthermore, we discuss some striking resemblances with temperature dynamics.     

Testing for cointegration at any frequency using spectral methods

Summary In this paper a new simple test for cointegration at any frequency is presented. This method can thus be applied to test for cointegration both at the zero and at the seasonal frequencies. It requires the estimation of the coherency spectrum of weakly stationary processes, therefore only standard spectral theory is involved. The testing procedure is similar to the one suggested by Phillips and Ouliaris (1988) and recently generalized by Joyeux (1992) to frequencies different from zero, but it does not suffer of some problems connected with the use of principal components methods in the frequency domain. Invited paper at the Conference held in Bologna, Italy, 27–28 May 1993, on ?Statistical Tests: Methodology and Econometric Applications?.     

震后交通管制下多出救点应急物资调运问题

销售季节性商品的零售企业在销售季节开始前通常仅能获得有限的市场信息,在销售过程中因滞销而产生剩余库存的现象十分普遍。因此,许多零售企业会将商品在一级市场销售之后以折扣价格在二级市场上继续销售。为此,建立了这一问题的两阶段模型,在较宽松的需求假设下分析了模型所具有的性质,通过求解得到了零售企业对季节性商品的订货量决策和商品在两级市场间转换时刻决策所要满足的最优条件。     

Seasonal host alternation by the andromeda lace bug,Stephanitis takeyai (Heteroptera: Tingidae) between its two main host-plant species

I studied the seasonal occurrence of the andromeda lace bug,Stephanitis takeyai, on its two main host-plant species. In a secondary forest in Kyoto, this bug altered its hosts seasonally, i.e., from an evergreen shrub,Pieris japonica, in winter to a deciduous shrub,Lyonia elliptica, in summer. In contrast, in Nara park where fewL. elliptica were available, the bug exploited onlyP. japonica. Thus, seasonal host alternation by this bug is not obligate. A comparison of adult longevity and fecundity on the two host-plant species demonstrated the higher quality ofL. elliptica as a food resource. Corresponding to this difference in host quality, there was a dramatic difference in the seasonal population growth in the two study sites. In Nara, the population size at the beginning of the 2nd generation was almost the same as in the overwintered generation, whereas in Kyoto the population size in the 2nd generation was approximately one hundred times as large as in the overwintered generation. Thus seasonal host alternation is adaptive for the bug. In a previous study, I reported that overwintering as eggs in living leaves of their hosts is likely to be common among all the related species of this bug. Thus, this trait can be considered to be a phylogenetic constraint to the group. I speculate that host alternation by this bug has been derived because it is more adaptive from autoecy on an evergreen plant, similar to the pattern currently found in Nara, and that this bug can not only exploit deciduous host due to a phylogenetic constraint.     

Bayesian Forecasting With Stable Seasonal Patterns

A multiplicative seasonal forecasting model for cumulative events in which, conditional on end- of-season totals being given and seasonal shape being known, it is shown that events occurring within the season are multinomially distributed is presented. The model uses the information contained in the arrival of new events to obtain a posterior distribution for end-of-season totals. Bayesian forecasts are obtained recursively in two stages: first, by predicting the expected number and variance of event counts in future intervals within the remaining season, and then by predicting revised means and variances for end-of-season totals based on the most recent forecast error.     

On Fan's adaptive Neyman tests for comparing two spectral densities

In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series.     

A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons

Flood events can be caused by several different meteorological circumstances. For example, heavy rain events often lead to short flood events with high peaks, whereas snowmelt normally results in events of very long duration with a high volume. Both event types have to be considered in the design of flood protection systems. Unfortunately, all these different event types are often included in annual maximum series (AMS) leading to inhomogeneous samples. Moreover, certain event types are underrepresented in the AMS. This is especially unsatisfactory if the most extreme events result from such an event type. Therefore, monthly maximum data are used to enlarge the information spectrum on the different event types. Of course, not all events can be included in the flood statistics because not every monthly maximum can be declared as a flood. To take this into account, a mixture Peak-over-threshold model is applied, with thresholds specifying flood events of several types that occur in a season of the year. This model is then extended to cover the seasonal type of the data. The applicability is shown in a German case study, where the impact of the single event types in different parts of a year is evaluated.     

中国经济增长趋势研究

1978年以来,中国经济高速增长,国民经济综合实力迅速增加,中国一跃成为世界第二大经济体,但是次贷危机对世界及中国经济造成了巨大影响,经过强有力的刺激政策,中国经济出现了短暂的恢复性增长,之后呈现逐渐下降态势.2014年中国GDP增长速度为7.4%,与经济危机之前的中国经济的高速增长相比有较大差距.采用季节调整模型和Hodrick-Prescott滤波分析方法研究发现,中国的长期经济增长速度呈现下滑趋势,其根本因素是消费、投资、净出口的拉动力下降,直接因素是房地产行业下行.为了保持中国经济的长期平稳增长,应该适当鼓励人口增长,降低税收和鼓励科技创新.     

Predicting rainy seasons: quantifying the beliefs of prophets

In general, meteorologists find it difficult to make seasonal predictions in the north-east region of Brazil due to the contrasting atmospheric phenomena that take place there. The rain prophets claim to be able to predict the seasonal weather by observing the behavior of nature. Their predictions have a strong degree of subjectivity; this makes science (especially meteorology) disregard these predictions, which could be a relevant source of information for prediction models. In this article, we regard the prophets’ knowledge from a subjectivist point of view: we apply elicitation of expert knowledge techniques to extract their opinions and convert them into probability densities that represent their predictions of forthcoming rainy seasons.