高精度灰色模型研究及2005年GDP总量预测

本文对灰色模型做了进一步的研究,拓广了灰色模型,建立了一个新的、预测精度较高的新灰色预测模型--"对数函数-幂函数变换"模型,并利用此模型对我国GDP总量做出精度较高的灰色预测.     

金融机构贷款灰色预测GM(1,1)模型研究

在市场经济条件下,研究金融机构贷款发展趋势具有重要意义。应用灰色系统理论与灰色预测方法,从金融机构贷款未来发展趋势预测出发,构建金融机构贷款灰色预测GM(1,1)模型,对山西省2011~2020年金融机构人民币各项贷款进行了预测分析,并对预测结果进行实际还原验证。预测结果较为满意,可供相关金融机构和经济部门决策时参考。     

灰色预测理论在房地产投资决策中的应用

预测是房地产投资决策的重要工作之一。文章在分析GM(1,1)模型和灰色马尔可夫预测模型两种灰色预测模型的基础上,结合实例对房地产价格和开发量进行预测,并取得了比较理想的结果。旨在提高房地产投资决策的科学性。     

电力市场中电力负荷灰色预测

传统灰色预测技术原理简单、样本量小、计算简便,适用于负荷预测,但其存在很多的局限性。文章针对传统灰色模型建立机理和存在的局限性,提出了改进方法,建立了新的店里负荷预测模型,通过实例计算表明,该模型具有良好的适用性和预测的精度,证明改进的效果较为显著。     

基于灰色Elman神经网络的季度性工业增加值动态预测方法的研究

工业增加值的准确预测对于政府部门制定工业发展政策有重要的作用。为更加准确地进行工业增加值的预测,以各个季度数据作为时间序列,建立四个灰色Elman预测模型进行预测,并同灰色GM(1,1)模型经行比较。实证分析:该模型不仅避免了季节性波动对预测的影响,而且缩短了预测步长,提高了预测精度和预测的稳定性。     

货运需求分析

城市物流需求分析在当前我国经济发展水平仍然落后、城市各方面发展不均衡的情况下具有重要的社会经济意义.本文分别采用多元线性回归预测法、指数平滑预测法和灰色预测法对2000年到2014年货运量的变化趋势进行了预测分析.通过对比三种预测方法,得到预测精度排序为:多元线性回归预测法>灰色预测法>指数平滑法.     

混沌理论在我国税收预测中的应用

本文利用非线性混沌动力学建立了税收的预测模型,对该模型的分岔、混沌及稳定性等特性进行分析,并且对我国1978～2001年的税收数据做了实证分析。结果表明:预测的精度达到了期望要求,明显高于非线性回归和灰色理论预测法的精度。     

贵州省人口老龄化灰色关联分析与预测

运用灰色关联分析,主成分分析和灰色预测对贵州省人口老龄化影响因素进行分析并作出预测。结果显示城镇化率和人口结构指标是主要的影响因素,社会发展和政府政策是重要影响成分,老龄化进程将加剧。     

黑龙江省GDP预测的灰色模型

灰色预测方法是经济预测中一种非常有效的方法,它的主要特点是建立预测模型所需要的原始数据不多,容易采集,这种预测方法简便并具有较高的准确性。本文运用灰色理论,以2000～2006年黑龙江省GDP情况为原始数据,建立了黑龙江省GDP预测的灰色模型,并对2007～2010年黑龙江省GDP做了预测。     

用灰色预测理论对经济管理活动的分析

本文探讨了灰色预测理论对现代经济管理活动进行预测分析的问题。运用灰色预测理论对企业某些活动进行预测分析,为经济管理决策提供参考。     

A GM(1,1)–Markov chain combined model with an application to predict the number of Chinese international airlines

In this paper, we propose a new dynamic analysis model which combines the first-order one-variable grey differential equation model (abbreviated as GM(1,1) model) from grey system theory and Markov chain model from stochastic process theory. We abbreviate the combined GM(1,1)–Markov chain (MC) model as MCGM(1,1) model. This combined model takes advantage of the high predictable power of GM(1,1) model and at the same time take advantage of the prediction power of Markov chain modelling on the discretized states based on the GM(1,1) modelling residual sequence. For prediction accuracy improvements, Taylor approximation is applied to MCGM(1,1) model. We call the improved version as T-MCGM(1,1) model. As an example, we use the statistical data of the number of Chinese international airlines from 1985 to 2003 for a validation of the effectiveness of the T-MCGM(1,1) model.     

基于灰色系统理论的旅游客源预测模型--以中国入境旅游客源为例

本文基于灰色系统理论,以中国1978年以来入境客源为例,建构了旅游客源预测的灰色模型,并与常用的线性模型的预测精度进行了定量对比。所得基本结论为:①旅游系统是灰色系统,灰色系统理论是研究旅游现象的有力工具;②中国入境客源灰色GM(1,1)模型的预测精度高于线性模型;③就灰色预测而言,如果原始数据列较长(例如21年),对原始数据进行平滑处理所得预测模型精度较高,而对原始数据进行取自然对数的处理所得模型预测精度较低;如果原始数据列较短(例如5年),对原始数据进行取自然对数的预处理所得模型预测精度较高。本文所得结论对其他旅游序列预测应该具有一定的借鉴与参考价值。     

基于灰色系统模型的城乡建设用地规模预测——以常德市鼎城区为例

土地利用总体规划是区域性的综合规划,为地方性的经济发展提供科学和法律依据.建设用地的有效供给是地方经济发展基础,建设用地需求量预测是土地利用总体规划编制的核心,也是土地利用管理的依据.为了切实搞好常德市鼎城区的土地利用总体规划,使之更具有前瞻性、可操作性,必须切合实际地搞好建设用地需求规模和布局的预测.采用灰色系统模型,通过灰色关联度分析,建设用地发展与GDP、总人口、市镇人口、社会固定资产投资、人均GDP、第二产业产值、第三产业产值密切相关.基于这些因子,采用灰色系统模型法、建立GM(1,1)灰色模型,进行了2010年和2020年的城乡建设用地规模的预测.GM(1,1)模型可以进行长期预测,但预测仅反映一种趋势,综合考虑驱动因素的灰色线性组合模型更具有预测的可靠性.利用GM(1,1)模型和线性模型组合,进行城乡建设用地规模预测,可为土地利用总体规划提供可靠的保证.     

基于灰色预测和神经网络的人口预测

人口预测是土地利用总体规划的重要基础工作。未来人口规模是土地利用总体规划中确定各类土地需求量控制性指标，调整土地利用结构，实现土地供需平衡，解决人地矛盾的重要依据。人口预测是否科学准确，直接关系到总体规划方案是否合理和实用。利用灰色预测建模所需信息少、方法简单的特点和神经网络具有较强的非线性映射能力的特性，提出一种基于灰色预测和神经网络的人口预测方法。首先对人口规模的NARMA(p，q)的递归网络模型进行一步预测及其灰色预测GM(1，1)等维新息模型预测，然后再用前馈神经网络对GM(1，1)模型和递归网络模型的预测值进行组合预测以作为其最终的预测值。以温州市为例，对其总人口进行了试验预测。结果表明：NARMA(p，q)递归网络模型比GM(1，1)模型具有更高的预测精度，而FNN模型的组合预测效果优于其它单一预测模型。     

Performance assessment for electronic manufacturing service providers using two-stage super-efficiency SBM model

Manufacturing Service providers (EMSs) offer services to Original Equipment Manufacturers (OEMs). However, increasing challenges require an EMS to be more capable, adaptable and responsive. For survival, an EMS manager has to understand its relative efficiency in the industry. In addition, an investor also requires such information for investments. In this research, we propose a novel approach, which combines GM(1,1) with a two-stage super-efficiency slacks-based measure (SBM) model, to forecast and assess the efficiencies of 18 EMSs. The GM(1,1) was first used to forecast future data of EMSs, and then the two-stage super-efficiency SBM model was used to measure the marketability and profitability efficiencies for an EMU in two stages. The results build a ‘past-current-future’ view on the two efficiencies for each EMS. In addition, the profitability efficiency can help justify the reasonability of marketability efficiency. Our results showed that Hon Hai tops the rankings in both profitability and marketability efficiencies. These results also provided information about relative efficiencies of these EMSs, which helps EMS managers and investors to make better decisions.     

灰色GM（1,N）模型在广东海洋经济预测中的应用

本文选取六个影响海洋经济快速发展的生产和需求因素,应用灰色系统理论建立了广东海洋经济GM（1,N）预测模型,并选择了线性回归、三次指数平滑和灰色GM（1,1）三种不同模型对六个影响因素指标进行预测,从而提高了预测模型的可靠性,为海洋经济定量预测分析提供了一种有效方法。从预测结果看,模型具有较高的拟合精度。     

中国海洋经济预测研究

海洋经济发展目标的确定是制定国家海洋发展战略、国家级海洋经济发展规划和沿海地方海洋经济发展规划中必须解决的问题之一。因此,海洋经济预测是海洋经济研究领域不可回避的问题之一。对适于进行海洋经济预测的灰色系统模型、趋势外推法、成长曲线法、组合预测法等进行深入探讨,对组合预测结果的最有效性给出可靠的数学证明,并运用所建立的模型对2015年和2020年我国海洋经济和主要海洋产业发展水平进行预测。     

The Laffer curve in schedular multi-rate income taxes with non-genuine allowances: An application to Spain

This paper models the connection between tax revenue and marginal tax rates in modern personal income taxes. In so doing, new analytical expressions for the elasticity of tax revenue to tax rates are derived taking into account global and schedular income taxes in the presence of non-standard allowances. Based on these new analytical elasticities the implicit Laffer curve is characterised and explored in detail. Calculations are performed for the individual taxpayer and the aggregate population. When applied to microdata, the model permits us to locate individually the position of every taxpayer on the entire range of the Laffer curve as well as to characterise the “representative” aggregate Laffer curve. The utility of the model to forecast revenue is illustrated by applying it to Spanish personal income tax. The model confirms that the Laffer curve is essentially an intrinsic individual matter although a virtual aggregate Laffer curve for the whole population can be inferred.     

Finance commission of India’s assessments: a political economy contention between expectations and outcomes

We explore the normative fiscal assessments of the Finance Commission (FC) of India, and realisation of fiscal policy with regard to Central Finances over the period 1990–2012. We employ the Theil’s inequality coefficient to investigate the magnitude of assessment errors and its partitioning in to bias, slope and random components. Furthermore, this article also evaluates the efficiency, biasedness and persistence of forecast errors. The robustness of the efficiency results are confirmed with the application of maximum entropy bootstrap. The objective of this study is to examine the structural basis on which FCs make their awards rather than examining the predictability of the forecasts. The story of FC’s assessments reflects an interesting political economy theatre of contention between aspirations and outcomes. Our key findings are as follows: First, source of errors for assessments of tax revenue, nontax revenue, interest payments, defence revenue expenditure, plan revenue expenditure and fiscal deficit is principally due to random component. However, the errors in the remaining economic parameters originate due to systematic components i.e. mean and slope errors. Second, the expenditure side predictability is lower than the revenue side predictability.     

Applying the Grey prediction model to the global integrated circuit industry

This study examines the precision of the Grey forecasting model applied to samples based on demand and sales in the global integrated circuit (IC) industry. In doing so, the main objective is to explore which forecast model is most appropriate for the IC industry by comparing the empirical results from the Grey model (GM), time series and exponential smoothing. Furthermore, three residual modification models are applied to enhance the forecasting results. Empirical results indicate that the GM is better suited to short-term predictions than to mid- and long-term predictions. Meanwhile, the Markov-chain residual modification model achieves reliable and precise results.