Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models

We evaluate forecasting models of US business fixed investment spending growth over the recent 1995:1–2004:2 out‐of‐sample period. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash‐Flow models of investment spending, as well as real stock prices and excess stock return predictors. The real stock price model typically generates the most accurate forecasts, and forecast‐encompassing tests indicate that this model contains most of the information useful for forecasting investment spending growth relative to the other models at longer horizons. In a robustness check, we also evaluate the forecasting performance of the models over two alternative out‐of‐sample periods: 1975:1–1984:4 and 1985:1–1994:4. A number of different models produce the most accurate forecasts over these alternative out‐of‐sample periods, indicating that while the real stock price model appears particularly useful for forecasting the recent behavior of investment spending growth, it may not continue to perform well in future periods. Copyright © 2007 John Wiley & Sons, Ltd.     

Statistical analyses of freeway traffic flows

This paper concerns the exploration of statistical models for the analysis of observational freeway flow data, and the development of empirical models to capture and predict short‐term changes in traffic flow characteristics on sequences of links in a partially detectorized freeway network. A first set of analyses explores regression models for minute‐by‐minute traffic flows, taking into account time of day, day of the week, and recent upstream detector‐based flows. Day‐ and link‐specific random effects are used in a hierarchical statistical modelling framework. A second set of analyses captures day‐specific idiosyncrasies in traffic patterns by including parameters that may vary throughout the day. Model fit and short‐term predictions of flows are thus improved significantly. A third set of analyses includes recent downstream flows as additional predictors. These further improvements, though marginal in most cases, can be quite radically useful in cases of very marked breakdown of freeway flows on some links. These three modelling stages are described and developed in analyses of observational flow data from a set of links on Interstate Highway 5 (I‐5) near Seattle. Copyright © 2002 John Wiley & Sons, Ltd.     

Additional information on Atherimorpha White (Diptera: Rhagionidae)

Material from Southern Chile and Patagonia was revised, and information on distribution and adult morphology of Atherimorpha imitans Malloch, A. nemoralis (Philippi), A. scutellaris Malloch and A. triangularis Malloch is given.     

Self‐Restraining Bass Models

We extend the classic Bass diffusion model to address the case in which existing adopters could depress the growth of adoption, i.e. the diffusion process is self‐restraining. Two modified Bass models are proposed according to whether the negative depressive effect is exerted on potential adopters or existing adopters. We then characterize the diffusion paths for several generalizations. Copyright © 2015 John Wiley & Sons, Ltd.     

以主体为中心的微博计算方法--微博计算微革命:从“信息”中心到以“人”为本

针对以微博为对象的分析挖掘,提出了“以人为本”的微博计算模型,即以微博主体为微博计算的主要对象,研究微博博主个性化表示模型,博主情绪感知算法、及微博内容分析等关键技术,综述了微博计算已有的研究进展.创新之处在于突破了纯粹内容分析的局限,更好地适应了微博计算的需求.     

Asymmetric larval competition between two species of neotropical foam‐nesting frogs: Leptodactylus fuscus and Engystomops pustulosus

Larval competition was investigated in foam‐nesting frogs with contrasting breeding strategies. Leptodactylus fuscus nests in burrows with moderate numbers of large eggs, mainly on dry nights; Engystomops pustulosus makes floating nests with large numbers of small eggs, mainly on wet nights. Both use the same temporary pools in open habitat over an extended breeding season. Larval growth to metamorphosis was assessed in a semi‐natural experiment and in the laboratory. Tanks contained either single species or equal numbers of both species. Inter‐specific competition was asymmetric; L. fuscus survived as well as or better in competition with E. pustulosus than in intra‐specific competition. Leptodactylus fuscus attained larger sizes with E. pustulosus present. In interspecific competition E. pustulosus showed worse survival and grew slower and to a smaller size. Outdoors, both species tolerated water temperatures that often rose to 42°C and reached metamorphosis in very short times: 14 days after oviposition in L. fuscus and 17 days in E. pustulosus.     

基于中心度的中国航空客流网络抗毁性分析

以中国航空客流为样本,运用复杂网络理论,构建中国航空客流网络,并论证其系统结构特性。从点度中心度、中间中心度和接近中心度角度确定网络的关键节点,采取基于3个中心度的蓄意攻击方式,根据网络整体效能和簇系数两项指标及其下降率的变化情况,对网络的抗毁性进行了实证分析。在可视化视角下,对比分析3种蓄意攻击方式下航空客流网络结构的变化情况。研究表明:目前中国航空客流网络面对3种蓄意攻击时,网络抗毁性较弱,结构呈现出不同的变化,为此应分别从3个中心度角度出发,着重确保网络关键节点的安全,增加更多的功能不同的关键节点,优化航线网络结构。     

Long‐memory dynamic Tobit models

We introduce a long‐memory dynamic Tobit model, defining it as a censored version of a fractionally integrated Gaussian ARMA model, which may include seasonal components and/or additional regression variables. Parameter estimation for such a model using standard techniques is typically infeasible, since the model is not Markovian, cannot be expressed in a finite‐dimensional state‐space form, and includes censored observations. Furthermore, the long‐memory property renders a standard Gibbs sampling scheme impractical. Therefore we introduce a new Markov chain Monte Carlo sampling scheme, which is orders of magnitude more efficient than the standard Gibbs sampler. The method is inherently capable of handling missing observations. In case studies, the model is fit to two time series: one consisting of volumes of requests to a hard disk over time, and the other consisting of hourly rainfall measurements in Edinburgh over a 2‐year period. The resulting posterior distributions for the fractional differencing parameter demonstrate, for these two time series, the importance of the long‐memory structure in the models. Copyright © 2006 John Wiley & Sons, Ltd.     

A non‐linear dynamic model for multiplicative seasonal‐trend decomposition

A non‐linear dynamic model is introduced for multiplicative seasonal time series that follows and extends the X‐11 paradigm where the observed time series is a product of trend, seasonal and irregular factors. A selection of standard seasonal and trend component models used in additive dynamic time series models are adapted for the multiplicative framework and a non‐linear filtering procedure is proposed. The results are illustrated and compared to X‐11 and log‐additive models using real data. In particular it is shown that the new procedures do not suffer from the trend bias present in log‐additive models. Copyright © 2002 John Wiley & Sons, Ltd.     

Neural network pruning applied to real exchange rate analysis

Neural networks are fitted to real exchange rates of several industrialized countries. The size and topology of the networks is found through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell. These pruned neural networks are good approximations to varying non‐linear trends in real exchange rates. Non‐linear dynamic analysis shows that the long‐term equilibrium values of several European currencies correspond to the actual values within the European Monetary System. Based on its long‐term equilibrium value, the Euro appears to be undervalued vis‐à‐vis the US dollar at the introduction of the Euro on 1 January 1999. Copyright © 2002 John Wiley & Sons, Ltd.