EMI滤波器的设计

文章从建立EMI滤波器的共模、差模噪声源等效电路及电路模型入手,进而探讨了EMI滤波器的设计方法及其在实际应用的结构和结构参数的选择问题。     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

Explicit filters and supply chain design

Due to the complexity of present day supply chains it is important to select the simplest supply chain scheduling decision support system (DSS) which will determine and place orders satisfactorily. We propose to use a generic design framework, termed the explicit filter methodology, to achieve this objective. In doing so we compare the explicit filter approach to the implicit filter approach utilised in previous OR research the latter focusing on minimising a cost function. Although the eventual results may well be similar with both approaches it is much clearer to the designer, both why and how, an ordering system will reduce the Bullwhip effect via the explicit filter approach. The “explicit filter” approach produces a range of DSS designs corresponding to best practice. These may be “mixed and matched” to generate a number of competitive delivery pipelines to suit the specific business scenario.     

Forecasting UK house prices: A time varying coefficient approach

Previous studies of UK house prices, developed from the demand and supply ofhousing or from the asset market approach have been poor in terms of robustness and ex-post forecasting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability. This article uses the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable. The estimation results of the TVC regression of UK house prices is compared with those obtained from three alternative constant parameter regressions. Comparisons of forecasting performance suggest the TVC regression out-performs forecasts from an Error Correction Mechanism (ECM), Vector Autoregressive (VAR) and an Autoregressive Time Series regression.     

曝气生物滤池及其填料作用机理

介绍了曝气生物滤池及其填料的应用情况和研究进展,对曝气生物滤池反应动力学以及其填料作用机理等方面的研究进展进行了研究,概述了影响曝气生物滤池运行的主要因素,同时提出了今后曝气生物滤池的发展方向。     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

近30年来我国粮食产量波动影响因素分析

[目的]根据粮食产量波动特征,从自然因素、科技因素(物质装备)、社会经济因素和农业生产条件等方面对粮食产量的影响进行量化分析,为科学合理地制定农业政策,保障我国粮食安全提供理论依据。[方法]文章利用HP滤波法分析1978—2010年我国粮食产量波动特征,并采用灰色关联与层析分析相结合的综合评价法定量分析不同波动周期各影响因子对我国粮食产量波动的影响度。[结果](1)改革开放以来,我国粮食产量经历了完整的6个波动阶段,平均波动周期为6年;(2)除1994—2000年,各波动周期的粮食产量受科技因素的影响最大,且影响日益显著,影响度从1978—1981年的37. 0%增加到2006—2010年的48. 3%,其中,有效灌溉面积的影响增大趋势最为明显,其次是农业机械总动力,化肥施用量的影响呈减弱趋势;(3)社会经济因素对粮食产量的影响呈波动性增加的规律,2006—2010年影响度达到24. 9%;(4)农业生产条件对粮食产量的影响在20世纪80年代初期与科技因素基本持平,此后,除1994—2000年,与科技因素的影响度差距越来越大,到2000—2006年对粮食产量的影响度仅为16. 6%,其中,耕地面积的影响度下降最多,到2006—2010年,影响度仅为1. 1%;自然因素对粮食产量波动的影响最小,且较为稳定,除1985—1988年影响度达到24. 3%,其余波动周期均在8. 7%~12. 4%之间,2006—2010年影响度为10. 2%。[结论]完善农田水利灌溉体系,加强对农业机械指导的"重科技"举措比单纯增加后备耕地数量、改善应对气候变化与自然灾害能力的"靠资源"发展更为有效;加大农田水利投入,完成灌区续建配套与灌排泵站更新改造是目前最重要的发展方向。     

沈四联污水处理的措施研究

沈阳采油厂的沈四联负责沈84块原油处理、原油外输以及污水处理工作,为了确保回注污水水质达到低渗区块的要求,针对污水处理中存在的实际问题提出改进方案,完善了过滤罐内部整体结构。同时采用新型滤料提高处理效果,对除油罐内部进水结构进行了优化改造,强化除油罐液位控制管理,沈四联的污水处理质量得到明显提高。