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1.
田萍 《数理统计与管理》2003,22(Z1):229-232
采用扩展的线性支出系统,运用回归分析方法,建立了河南省城镇居民消费需求函数模型,并对河南省城镇居民的消费需求结构进行了统计分析.  相似文献   

2.
基于扩展线性支出系统(ELES)的浙江农民消费需求变化分析   总被引:1,自引:0,他引:1  
对先进的经济计量方法—扩展的线性支出系统进行解析.运用该模型及2002年—2006年浙江农民收入和消费数据,对浙江农民的消费结构、消费倾向和消费需求弹性进行分析.在此基础上提出相应建议.  相似文献   

3.
湖南省城镇居民消费结构的实证分析   总被引:1,自引:0,他引:1  
赵志坚 《经济数学》2005,22(4):403-409
文章以湖南统计年鉴提供的数据资料为依据,建立了扩展的线性支出系统模型,利用该模型,计算了湖南省城镇居民年基本需求支出量、边际消费倾向、需求收入弹性和需求自价格弹性。根据这些计算结果分析了湖南省城镇居民消费结构中存在的问题,并提出了相应的对策。  相似文献   

4.
本文运用扩展的线性支出系统模型研究了我国“九五”期间城乡居民的消费结构状况。对城乡居民的消费结构和变化趋势进行了比较 ,同时给出了消费的合理性政策建议。  相似文献   

5.
重庆直辖以来,社会经济得到快速发展,城乡居民收入持续稳定增长,其消费观念、消费支出结构发生了巨大的变化,为进一步把握重庆市城乡居民消费支出结构的特征、差异和变动规律,运用扩展线性支出系统(ELES)模型,从边际消费倾向、基本需求分析和需求收入弹性分析三个方面进行比较分析,结果表明重庆城乡二元经济特征明显,农村居民消费能力较弱、消费结构明显滞后于城镇居民,并在此基础上提出了提高农村居民收入,优化消费结构,改善消费环境、完善城乡社会保障体系等对策建议.  相似文献   

6.
向琳 《运筹与管理》2001,10(2):158-162
用logistic曲线方程模拟我国居民人均收入、消费支出变化情况,并用该模型对其作短期预测,同时用该模型对居民收入和消费支出变化状况作出了阶段分析,进一步为经济发展作宏观分析和预测提供信息和根据。  相似文献   

7.
周亚军  陈建华 《经济数学》2012,29(4):99-104
通过对经常项目的跨时最优现值模型进行扩展,将居民的消费习惯变量包含进了扩展模型并进行了实证检验.结果表明,模型的功效得到了显著改善,居民的消费习惯在中国经常项目的差额波动路径中起了重要作用.由于消费习惯的形成,居民更加关心消费的变化而不是消费水平,其跨时消费决策的结果则是储蓄大于投资.因此,缩小居民的收入差距、降低对未来的支出预期,逐步转变居民的消费习惯是调整中国经常项目差额波动的有效途径.  相似文献   

8.
以1993-2007年数据为根据,采用逐步回归方法,建立中国城镇居民消费支出的多元非线性回归模型.结果表明:影响居民消费支出的主要因素有收入、消费意愿、居住面积、商品零售价格.消费支出随着收入、消费意愿、商品零售价格的提高而提高,随着人均居住面积的增加而先增后减.而且多元非线性回归模型比线性回归模型更能准确描述客观实际结果.  相似文献   

9.
扩展线性支出系统在山西城镇居民消费结构分析中的应用   总被引:19,自引:0,他引:19  
近年来,随着收入水平的不断提高,山西省城镇居民的消费结构发用扩展线性支出系统对消费结构做了系统的分析。  相似文献   

10.
消费是经济增长的原动力,消费需求作为最终需求,在经济总量中占主导地位,对经济增长具有重要的拉动作用.运用扩展线性支出系统模型,对新疆居民需求消费结构进行了分析,在此基础上实证分析了新疆居民消费对经济增长的贡献度.实证结果表明:新疆居民消费对经济增长的贡献越来越低,且农村居民消费相对于城镇居民消费对经济的贡献作用较小.最后提出了增加居民收入、拓展消费空间、扩大居民消费和开拓农村市场等提升新疆居民消费对经济增长贡献的政策建议.  相似文献   

11.
This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.  相似文献   

12.
基于小波神经网络的中国能源需求预测模型   总被引:2,自引:0,他引:2  
王珏  鲍勤 《系统科学与数学》2009,29(11):1542-1551
通过分析影响我国能源需求的主要因素,建立了基于小波神经网络的需求预测模型.采用定性与定量相结合的方式,分析了影响我国能源需求的主要因素,通过将人口总数、GDP、产业结构变化以及能源消费量的一阶滞后作为输入变量,建立基于小波神经网络的我国能源需求非线性预测模型.实验结果表明,该非线性预测模型与多元回归模型相比更加合理,具有更高的预测精度.  相似文献   

13.
We consider forecasting in systems whose underlying laws are uncertain, while contextual information suggests that future system properties will differ from the past. We consider linear discrete-time systems, and use a non-probabilistic info-gap model to represent uncertainty in the future transition matrix. The forecaster desires the average forecast of a specific state variable to be within a specified interval around the correct value. Traditionally, forecasting uses a model with optimal fidelity to historical data. However, since structural changes are anticipated, this is a poor strategy. Our first theorem asserts the existence, and indicates the construction, of forecasting models with sub-optimal-fidelity to historical data which are more robust to model error than the historically optimal model. Our second theorem identifies conditions in which the probability of forecast success increases with increasing robustness to model error. The proposed methodology identifies reliable forecasting models for systems whose trajectories evolve with Knightian uncertainty for structural change over time. We consider various examples, including forecasting European Central Bank interest rates following 9/11.  相似文献   

14.
王飞 《经济数学》2011,28(2):95-100
由于缺乏足够的观测数据等原因,常规的区域经济预测模型在我国难以获得预期的预测效果,而贝叶斯向量自回归(BVAR)模型将变量的统计性质作为参数的先验分布引入到传统的VAR模型中,能够克服自由度过少的问题,以青海为例,本文建立了一个BVAR模型,并引入了全国GDP和中央政府转移支付作为外生变量以描述国民经济与区域经济的联系...  相似文献   

15.
Short-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.  相似文献   

16.
Handling forecasting problems using fuzzy time series   总被引:10,自引:0,他引:10  
In [6–9], Song et al. proposed fuzzy time-series models to deal with forecasting problems. In [10], Sullivan and Woodall reviewed the first-order time-invariant fuzzy time series model and the first-order time-variant model proposed by Song and Chissom [6–8], where the models are compared with each other and with a time-invariant Markov model using linguistic labels with probability distributions. In this paper, we propose a new method to forecast university enrollments, where the historical enrollments of the University of Alabama shown in [7,8] are used to illustrate the forecasting process. The average forecasting errors and the time complexity of these methods are compared. The proposed method is more efficient than the ones presented in [7, 8, 10] due to the fact that the proposed method simplifies the arithmetic operation process. Furthermore, the average forecasting error of the proposed method is smaller than the ones presented in [2, 7, 8].  相似文献   

17.
The support vector regression (SVR) is a supervised machine learning technique that has been successfully employed to forecast financial volatility. As the SVR is a kernel-based technique, the choice of the kernel has a great impact on its forecasting accuracy. Empirical results show that SVRs with hybrid kernels tend to beat single-kernel models in terms of forecasting accuracy. Nevertheless, no application of hybrid kernel SVR to financial volatility forecasting has been performed in previous researches. Given that the empirical evidence shows that the stock market oscillates between several possible regimes, in which the overall distribution of returns it is a mixture of normals, we attempt to find the optimal number of mixture of Gaussian kernels that improve the one-period-ahead volatility forecasting of SVR based on GARCH(1,1). The forecast performance of a mixture of one, two, three and four Gaussian kernels are evaluated on the daily returns of Nikkei and Ibovespa indexes and compared with SVR–GARCH with Morlet wavelet kernel, standard GARCH, Glosten–Jagannathan–Runkle (GJR) and nonlinear EGARCH models with normal, student-t, skew-student-t and generalized error distribution (GED) innovations by using mean absolute error (MAE), root mean squared error (RMSE) and robust Diebold–Mariano test. The results of the out-of-sample forecasts suggest that the SVR–GARCH with a mixture of Gaussian kernels can improve the volatility forecasts and capture the regime-switching behavior.  相似文献   

18.
Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8% accuracy gains over the current forecasting process.  相似文献   

19.
Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctuations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new operator can better capture the real developing trend in raw data and improve forecast accuracy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy.  相似文献   

20.
应用果蝇优化算法对径向基神经网络扩展参数的优化方法进行研究,给出了一种以标准误差计算公式为味道判定函数,以此确定最优的径向基函数的扩展参数值的方法,并建立了相应的预测模型.应用该预测模型对黑龙江省外贸出口额进行预测,结果表明:预测模型的预测精度优于径向基神经网络,从而证明了方法的有效性.  相似文献   

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