The future of the mobile phone business

One of the fastest growing technologies of our times is that of mobile phones. In this article we use the assumption that the diffusion of mobile technology, as measured by the number of active mobile accounts, follows the well known S-curve of natural growth in competition systems. The accuracy of the logistic fit is tested against actual data for the whole world, Europe, China and the GSM system. Using the produced models predictions concerning the future of mobile business are deliberated.According to these models active mobile accounts around the globe are expected to grow from 1.7 billion in 2004 to approximately 2 billion in 2008, reaching a peak penetration of 29.2%. Growth barriers, apart from the age of the potential user, are also low income and extreme poverty. Europe, early adopter of mobile technology and leader in active mobile accounts against all other regions in the world, has apparently reached a peak with almost every European, apart from the very young or very old, using a mobile phone. The mobile market in China is anticipated to exceed 500 million active accounts and may increase even further depending on the economic and social reform that is currently under way in that part of the world. GSM will most likely remain the leading mobile technology in the future as it is today.The growth process for the world, Europe, and the GSM system is almost completed and during this stage instabilities may occur before the potential emergence of a new wave of growth.     

港口货流预测分析及软件技术

物流技术随着电子商务的崛起已逐渐成为新经济研究的焦点。随着入世的临近及入世的实现，作为物流中心枢纽的港口，其货（物）流将会发生极大的变化，无论是货种、流向还是货物公司及运输方式、运输始发地等，都会有较大的改变，迫切需要行之有效的预测方法及软件。基于灰色理论的预测方法较传统的统计预测方法有较大的不同，它不需要多数据统计的样本，是解决少数据不确定系统预测的有效方法，对我国新港多，港口统计数据不稳定不全面等情况尤为合适。本预测分析软件运用最新的数据库与界面技术，使得预测快捷、直观、界面友好。     

基于LINGO优化的灰色模型在旅游需求预测中的应用

提出了一种基于LINGO优化的灰色模型（简称LINGOGM），并将LINGOGM模型运用于桂林市的旅游需求预测。与传统的GM（1，1）模型相比，LINGOGM模型在旅游需求预测中具有较高的预测精度和较广的应用范围。     

The accuracy of long-term earnings forecasts in the electric utility industry

This paper examines the accuracy of various methods of forecasting long-term earnings growth for firms in the electric utility industry. In addition to a number of extrapolative techniques, Value Line analyst forecasts are also evaluated. Value Line analyst forecasts for a five-year time horizon are found to be superior to many of the extrapolative models. Among the extrapolative models examined, implied growth and historical book value per share growth rate models performed best. These results provide strong support for using Value Line growth forecasts in cost of capital estimates for electric utilities in the context of utility rate cases. Value Line forecast errors could be explained by changes in dividend payout ratios, the firm's regulatory environment and bond rating changes.     

区域物流需求预测的LaOR方法

目前回归函数中普遍存在的泛化能力得不到保证的缺点,结合统计学习理论的研究成果,建立了基于最小一乘准则的最优回归模型(LaOR模型)。与以往回归模型相比较,新模型综合考虑了回归误差和置信范围,可望有效地降低回归模型的期望风险。上海市将LaOR应用到物流需求的短期预测中,取得了可以接受的预测效果。     

回归模型异方差性分析

介绍了预测模型的异方差性的概念，产生的原因以及常用的对模型的异方差性的检验方法。并介绍了模型异方差性消除的方法和程序。     

Housing and the Macroeconomy: Inflation and the Financial Accelerator

Three themes connecting housing and the macroeconomy are discussed. First, evidence is presented for the property market as one of the drivers of U.S. consumer price inflation. Second, key drivers of house prices are explained to account for the remarkable diversity of international experience. Finally, three potential links between housing, credit, and the financial accelerator are discussed. These are the consumption channel, the investment channel, and feedback between bad loans and risk‐spreads via the financial system—and how institutional differences between countries can explain the presence, absence and magnitudes of these linkages.     

海南国内生产总值的统计预测模型

国内生产总值(GDP)是衡量一个国家或地区经济实力的指标。建立了海南建省以来的GDP预测模型,并通过时间序列模型得到自变量的预测年度数据,进而对海南"十一五"与"十二五"期间的国内生产总值进行了预测,为制订经济发展规划提供参考。     

Epistemological foundations for the assessment of risks in banking and finance

This article presents the epistemological and conceptual foundations on which current attempts to model crises and assess financial risks are based. It draws a distinction between two research programs, in Lakatos' sense: on the one hand, crises understood as structural events within a cycle; on the other hand, crises seen as statistical tail events. The methodological, theoretical and practical consequences of such a dichotomy are exposed. A crucial difference lies in the assumptions about change in the causal processes generating economic outcomes, especially asset returns. Furthermore, this article insists on providing conceptual definitions of key terms that have distinct meanings within the two research programs.     

Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market

Marketing managers have to forecast the market size and this forecast guides strategic decisions whether to continue exporting, open new factories or expand existing production operations. Forecasting sales and the market size is a challenging task; even more so in emerging markets where data is limited and the market demand is changeable. This research proposes a novel approach that applies diffusion models using car ownership data to forecast car sales. Car ownership data may be easier to access than sales data in emerging markets but marketing managers are more interested in the sales forecast. Researchers propose using diffusion models to forecast the adoption of new products or products which are new to consumers in a market. This research demonstrates that marketing managers can use diffusion models to predict car sales in China where cars are new products to most consumers in this market. Since the majority of car buyers in China are first time buyers, car manufacturers and retailers must also forecast when the market composition will change. This effectively means predicting when first time car buying will start to slow down and repeat/replacement purchase or second hand car purchase will become more important. To forecast both sales and market composition change, marketing managers must choose a robust model. Managers want insights from models that have been tested robustly especially in less stable market conditions. In this context, this study illustrates the value of using a rolling forecast instead of a fixed horizon approach when comparing and choosing which model to use to forecast both sales and market composition change for the Chinese car market.