The Marshallian macroeconomic model: A progress report

In this progress report, we first indicate the origins and early development of the Marshallian macroeconomic model (MMM) and briefly review some of our past empirical forecasting experiments with the model. Then we present recently developed one-sector, two-sector and n-sector models of an economy that can be employed to explain past experience, predict future outcomes and analyze policy problems. The results of simulation experiments with various versions of the model are provided to illustrate some of its dynamic properties that include “chaotic” features. Last, we present comments on planned future work with the model.     

An Interval Fuzzy Multiobjective Watershed Management Model for the Lake Qionghai Watershed, China

Integrated watershed management is required to ensure the reasonable use of resources and reconcile interactions among natural and human systems. In the present study, an interval fuzzy multiobjective programming (IFMOP) method was used to solve an integrated watershed management problem. Based on system analysis, an IFMOP model suitable for a lake watershed system {IFMOPLWS} was developed and applied to the Lake Qionghai watershed in China. Scenario analysis and an interactive approach were used in the solution process. In this manner, various system components were incorporated into one framework for holistic consideration and optimization. Integrality and uncertainty, as well as the multiobjective and dynamic characteristics of the watershed system, were well addressed. Using two scenarios, two planning schemes were generated. Agriculture, tourism, macroeconomics, cropland use, water supply, forest coverage, soil erosion, and water pollution were fully interpreted and compared to identify a preferable planning alternative for local agencies. This study showed that the IFMOPLWS is a powerful tool for integrated watershed management planning and can provide a solid base for sustainable watershed management.     

Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.     

An investigation of transformation-based prediction interval for the Weibull median life

Statistical inference based on the Weibull distribution, a distribution widely used in reliability and survival analysis, is usually difficult as it often involves numerical computation and approximation. However, this distribution can be transformed to near-normality by a simple power transformation. Based on this transformation, a prediction interval (PI) for its median can be easily constructed through an inverse transformation. The procedure for selecting the best power transformation through minimizing Kullback-Leibler information is described. The property of this transformation-based PI is investigated. Simple correction factors are also proposed. It is shown that the transformation-based PI with corrections performs well, irrespective of the sample size and parameter values. Simulation results show that the new PI generally outperforms the existing PI. Numerical examples are given for illustration.     

回归分析法在经济预测中的应用浅析

回归分析法是在经济预测中常用的方法之一，它是在观察和分析经济发展的历史和现状的基础上，按照一定的方式建立反映其关系的数学模型，然后根据自变量在未来的变化来计算预测变量的变化，从而对未来的经济发展趋势进行预测。其关键是建立回归模型，并进行相关分析和结果预测。     

社会预测体制的生成与演变

预测活动是现代人类活动的基础,科学地预见未来与人类的命运休戚相关。该文在梳理预测体制形成的历史流变的基础上,着重探讨了现代预测体制的确立及其在领导体制中的重要地位,特别是现代预测体制中的国家预测系统已成为国家安全的重要依托之一。正是由于各国建立了日趋完善的预测体制,现代国际政治关系才得以形成并稳定下来。现代预测体制正朝着控制整个社会体制的方向发展。现代社会中所有机构的决策却越来越依赖于预测;社会体制的改革是预测体制行为新范式的灵魂。     

基于区间两阶段随机规划方法的 北京市水资源优化配置

针对缺水型城市水资源配置过程中的不确定性,建立了基于不确定性基本理论的区间两阶段随机规划模型,应用于北京市2025年水资源优化配置的研究。以北京市用水综合效益最大为目标函数,引入概率分布和区间数表示多重不确定性,求得北京市2025年城六区与郊区在生活、工业、农业与生态用水上的优化供水目标以及不同来水水平下的配水方案。结果表明:北京市2025年优化供水目标为47.39亿m~3,城六区工业用水与郊区农业用水的供水目标应采取保守值;不同来水水平下的优化配置水量为[36.49,47.39]亿m~3,仅北京为丰水年时不存在缺水现象,北京与丹江口水库同时遇枯时的缺水量高达[5.48,10.90]亿m~3,对北京市供水安全造成极大的威胁。该模型充分考虑不确定因素对水资源配置的影响,权衡用水收益与缺水风险的关系,并以区间的形式给出配置结果,可为北京市2025年供水目标与水资源优化配置方案的制定以及水资源安全保障措施的分析提供科学依据。     

总量均衡区间与宏观调控应确立的若干新原则

现代市场经济条件下,供给方调整其生产规模所需的成本可称为调节成本。由于存在调节成本,传统分析中的“总量均衡中心点”两侧便分别存在着“低位临界点”与“高位临界点”,这两点界定了一个特殊的“总量均衡区间”。“总量均衡区间”的出现对宏观调控将产生诸多重要影响,使之面临一系列新问题、新挑战。为有效应对这种新情况,在宏观调控实践中有必要相应地确立“近似均衡原则”、“适度超前原则”、“临界点原则”、“供给方原则”。     

基于河道径流可变区间的河流水资源可开发利用率分析

为了确定水电开发活动对河流水资源的可开发利用率,建立分期展布的河道径流可变区间核算方法,其中河道径流包括河道最小生态需水和河道最大洪流,并以西藏拉萨河为例进行核算。结果表明:拉萨河河道最小生态需水和最大洪流年内动态变化分别为29.5~328.3 m3/s和95.1~1 673.4 m3/s。与河道最小生态需水约束相比,河道径流可变区间约束使得拉萨河年调节型水电开发的水资源可开发利用率从60.2%下降到18.7%。指出对于径流丰枯特征十分明显的季节性河流,大型水利工程在平衡径流季节分布的过程中,应该受河道径流可变区间约束,尤其是枯水期最大洪流约束下河道径流量的可增加空间。     

基于贝叶斯模型加权平均方法的水文模型不确定性分析

贝叶斯模型加权平均（BMA）方法是通过综合几个模型预报值的后验分布来推断预报量的更可靠概率分布分析工具。它不仅能提供一个综合的预报值,还能提供一个综合的预报区间。本文采用3个水文模型,统一用SCE-UA算法率定参数,得到3组不同的预报值用于BMA方法的综合,着重分析比较BMA和单个模型的预报不确定性区间,来检验贝叶斯模型加权平均方法是否能提高预报的可靠性。结果表明,BMA方法不仅能提高预报精度,还能推求出性质更为优良的预报区间,提高预报的可靠性。