DELAY ESTIMATION BY A HILBERT TRANSFORM METHOD

This paper describes an estimation of the time delay between two stationary time series signals, in which an input signal is measured with little noise and an output signal is the sum of a noise and the response from a linear system. We use the Hilbert transform relation for minimum delay systems to estimate the time delay. Some computer simulation results are given to evaluate the performance of the proposed method.     

An Advanced Heuristic for Multiple‐Option Spare Parts Procurement after End‐of‐Production

After‐sales service is a major source of profit for many original equipment manufacturers in industries with durable products. Successful engagement in after‐sales service improves customer loyalty and allows for competitive differentiation through superior service like an extended service period during which customers are guaranteed to be provided with service parts. Inventory management during this period is challenging due to the substantial uncertainty concerning demand over a long time horizon. The traditional mechanism of spare parts acquisition is to place a large final order at the end of regular production of the parent product, causing major holding costs and a high level of obsolescence risk. With an increasing length of the service period, more flexibility is needed and can be provided by adding options like extra production and remanufacturing. However, coordinating all three options yields a complicated stochastic dynamic decision problem. For that problem type, we show that a quite simple decision rule with order‐up‐to levels for extra production and remanufacturing is very effective. We propose a heuristic procedure for parameter determination which accounts for the main stochastic and dynamic interactions in decision making, but still consists of relatively simple calculations that can be applied to practical problem sizes. A numerical study reveals that the heuristic performs extremely well under a wide range of conditions, and therefore can be strongly recommended as a decision support tool for the multi‐option spare parts procurement problem. A comparison with decision rules adapted from practice demonstrates that our approach offers an opportunity for major cost reductions.     

逆防卫的许禁之争及其适用条件分析

原不当侵害人对不当防卫能否进行逆防卫,理论上存在赞成与反对两种观点。当面临生命或重大健康安全时人会本能地反击,这是人性的表面,国外立法和我国正当防卫制度都没有否定原不法侵害人的逆防卫权。着眼于加害与被害的关系,否定逆防卫人的逆防卫权有脱离现实之嫌。但逆防卫毕竟是一种特殊的防卫,如果按正当防卫的一般条件要求逆防卫人,不利于原防卫人权利的保护,因而还必须为其设置一些特殊的限制条件。从逆防卫的特殊性出发,除满足正当防卫的一般条件外,逆防卫的成立还要满足四个条件：一是不当防卫必须严重危及人身安全;二是严重危及人身安全的不当防卫非常紧迫;三是对严重危及人身安全的暴力犯罪不允许进行逆防卫;四是逆防卫人必须履行躲避义务。     

江苏进出口贸易与经济增长关系的实证研究

从2000-2013年的GDP和进出口数据出发,运用HP滤波、相关分析、偏相关分析、协整理论和Granger因果检验,对江苏省进出口与经济增长的关系进行实证分析可见,出口额、进出口总额与江苏省GDP的增长均具有较为显著的正向关系。通过活跃外贸活动扩大出口仍将是推动江苏经济增长的主要因素之一。     

对中国股市的信息不对称现象的分析

中国股市波动性很大,存在过量的噪声交易,其主要原因之一是信息不对称.Eas-lev,O'Hara,Paperman模型可以对信息不对称现象作一个量上的检测,有助于监管部门提高对股市规范化的监督能力.     

美国大都市区的阶级和种族隔离与开放郊区运动

由于美国郊区化过程中的过滤机制,形成了美国大都市区独特的人口分布模式,即中心城市与郊区之间的阶级和种族隔离,这就使聚集在中心城市的下层阶级和少数民族面临着极大的困境.因此,美国在六七十年代开展了"开放郊区运动".但是,由于根深蒂固的阶级偏见和种族歧视,开放郊区运动步履维艰,并最终走向失败.     

A Bayesian Time Series Model of Multiple Structural Changes in Level,Trend, and Variance

We investigate whether seasonal-adjustment procedures are, at least approximately, linear data transformations. This question was initially addressed by Young and is important with respect to many issues including estimation of regression models with seasonally adjusted data. We focus on the X-11 program and rely on simulation evidence, involving linear unobserved component autoregressive integrated moving average models. We define a set of properties for the adequacy of a linear approximation to a seasonal-adjustment filter. These properties are examined through statistical tests. Next, we study the effect of X-11 seasonal adjustment on regression statistics assessing the statistical significance of the relationship between economic variables. Several empirical results involving economic data are also reported.     

The Modeling and Seasonal Adjustment of Weekly Observations

Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time series model that allows the seasonal pattern to evolve over time and enables trend extraction and seasonal adjustment to be carried out by means of state-space filtering and smoothing algorithms. The method is illustrated with a Bank of England series on the money supply.