Forecasting economic time series with unconditional time-varying variance

The classical forecasting theory of stationary time series exploits the second-order structure (variance, autocovariance, and spectral density) of an observed process in order to construct some prediction intervals. However, some economic time series show a time-varying unconditional second-order structure. This article focuses on a simple and meaningful model allowing this nonstationary behaviour. We show that this model satisfactorily explains the nonstationary behaviour of several economic data sets, among which are the U.S. stock returns and exchange rates. The question of how to forecast these processes is addressed and evaluated on the data sets.     

Bayesian inference in time series models using kernel quasi likelihoods

The paper takes up Bayesian inference in time series models when essentially nothing is known about the distribution of the dependent variable given past realizations or other covariates. It proposes the use of kernel quasi likelihoods upon which formal inference can be based. Gibbs sampling with data augmentation is used to perform the computations related to numerical Bayesian analysis of the model. The method is illustrated with artificial and real data sets.     

我国科技投入效率的省际比较分析

文章运用DEA方法,对我国31个省市科技产出的截面数据进行了实证分析,针对经典DEA模型存在的缺陷,采用四阶段DEA结合Tobit回归的方法,剔除了外生环境变量的影响,衡量和评价了各地区的科技投入效率。结果表明:环境因素对科技投入效率影响显著,经典DEA方法得出的科技投入效率被明显高估;排除环境变量后,绝大部分省市的规模无效率是由于科技投入不足造成的。     

Airline flight networks, cycle times, and profitability: 2004–2006

This research investigates the relationships between airline flight networks, aircraft cycle times, and carrier profitability for ten large US domestic airlines. We find that direct point-to-point flight networks and short cycle times are operational factors that airlines should exploit to improve profitability. These findings are based on the analysis of 11.9 million flight records from 2004 through 2006. The results contradict earlier research validating performance advantages of hub-and-spoke flight networks. Today, the advantages of passenger consolidation at hub airports are lost to lower aircraft utilization and productivity problems created by the extended cycle times at hubs. We also find that airline operating profit can be increased by improving the efficiency of the aircraft turnaround process and decreasing aircraft fleet complexity. This research also provides an estimate of the marginal opportunity cost of ground time. A 1-min reduction in aircraft fleet ground time increases the average sized carriers’ operating income by $12 to $18 million.     

关于NBA赛程利弊因素的分析与评价模型

本文考虑球队间胜率、主场优势、休整时间三个因素来分析赛程对某一支球队的利弊。建立主场优势、休整时间同球队间胜率的变化量之间的多元线性回归模型:△Pij=β0+β1x1+β2x2+β3x3+β4x4+ε。通过赛程数字化方法得出某一球队的回归变量矩阵[x1、x2、x3、x4]及预测变量矩阵[△Pij],做回归分析得到回归系数并定义了数量指标评价赛程对球队的利弊,并给出了计算某一球队值的算法。     

利用气井历史生产数据计算动态储量新方法

气藏动态储量是指参与渗流的地下气体地质储量,计算动储量常采用物质平衡法,但是对于难以获得平均地层压力的致密气藏不适用。本文介绍了利用气井长时间生产数据计算动态储量的方法,并研究采用叠加时间函数(Super-t)分析变产量修正时间,使计算结果更加精确;同时应用该方法对苏东南区致密砂岩气藏部分气井进行单井动态储量计算,为该区动储量估算提供借鉴。     

同异反网络计划中工序时间的计算与控制

同异反网络计划方法是一种有别于传统但更加符合工程实际的新型网络计划方法,它能表达和处理网络计划中工序变量因随机、模糊、不确知和中介等多种不确定性以及突发性。本文在推广同异反联系数概念的基础上,提出了在多种影响因素下工程工期的预测方法,并分析每种因素对工程关键路线造成的影响以进行相应的控制。     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.