基于熵的房地产开发企业核心竞争力评价的组合权值方法

分析了目前房地产开发企业核心竞争力评价方法存在的问题,提出了评价指标的组合权值法,建立了评价指标体系,并进行了实际评价,指出了此方法的科学合理性。     

构建高校"两课"教学新模式--运用网络教育与传统教育互补优势提高"两课"教学效果

在以科技为主导的21世纪,网络多媒体教学已成为教育领域新兴的重要教学模式,将网络教育引入到“两课”教学建设中,探讨二者的优势互补作用,对激发“两课”的活力,充分发挥“两课”对大学生进行思想政治教育的主渠道和主阵地的作用是十分必要的。     

构建高校大学生党员教育新模式探索

为进一步提升高校党建实效,高校大学生党员教育工作亟待创新型机制,笔者以高校志愿工作为突破口,通过分析志愿工作之现状,探索其和学生党员教育工作的联动机制,旨在为当前学生党员教育工作注入新的生机和活力。     

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900 to 2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009).We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree.Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts – including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.     

Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO”). Ex post analysis reveals that the optimal solution has a very simple form: the vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts; indeed, they perform approximately as well as the ex post best forecaster.     

Estimating portfolio risk for tail risk protection strategies

We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, copula‐GARCH and dynamic generalized autoregressive score models. Utilizing a loss function that overcomes the lack of elicitability for expected shortfall, we propose a novel expected shortfall (and value‐at‐risk) forecast combination approach, which dominates simple and sophisticated standalone models as well as a simple average combination approach in modeling the tail of the portfolio return distribution. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts, given the defensive portfolio insurance mechanics.     

GROEC: Combination method via Generalized Rolling Origin Evaluation

Combination methods have performed well in time series forecast competitions. This study proposes a simple but general methodology for combining time series forecast methods. Weights are calculated using a cross-validation scheme that assigns greater weights to methods with more accurate in-sample predictions. The methodology was used to combine forecasts from the Theta, exponential smoothing, and ARIMA models, and placed fifth in the M4 Competition for both point and interval forecasting.     

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogeneous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to simultaneously approximate long memory behavior, as well as describe sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.     

集装箱多式联运系统中各种运输方式最优组合模式研究

针对集装箱多式联运系统中各运输方式的最优组合模式进行了研究。首先,通过对集装箱多式联运网络的描述和相应变形,建立了多式联运虚拟运输网络,然后以多式联运网络系统性能指标(PI)最优为目标建立了模型,并采用改进的遗传算法作为求解算法。系统性能指标(PI)定义为各运输方式运输费用和运输时间的线性加权之和,针对不同货物可分别赋予不同的权重系数。最后,通过对一个实例问题的求解验证表明,本文方法有效地从定量角度分析了集装箱多式联运过程中各种运输方式的最优组合模式,为集装箱多式联运的相关研究工作提供了理论基础。     

上海浦东加快金融服务外包基地建设之我见

本文紧贴上海浦东金融服务外包发展的实际,在对上海浦东发展金融服务外包的优势、机遇和挑战进行详细分析的基础上,概括出上海浦东加快金融服务外包发展的基本走向与框架,并根据这一宏观趋势提出了产学研开放推进浦东金融服务外包的四维构想,从金融服务外包产业发展研究、企业成长服务、专业人才培养、国际交流论坛等四个方面,为上海浦东加快发展金融服务外包基地建设提出了切实可行的建议。