长期融资与经济增长

三十人小组（G30）将“投资时间区间”作为2012年的研究课题，形成《长期融资与经济增长》报告。报告旨在测算未来全球长期融资需求的规模，发现影响金融体系提供长期融资进而影响未来经济增长的潜在障碍，并提出了促进长期融资的五个目标以及具体建议。     

Time varying country risk: an assessment of alternative modelling techniques

Three different techniques for the estimation of a time-varying beta are investigated: a bivariate GARCH model, the Schwert and Seguin approach, and the Kalman filter method. These approaches are applied to a set of monthly Morgan Stanley country index data over the period 1970 to 1995 and their relative performances compared. In-sample forecast tests of the performance of each of these methods for generating conditional beta suggest that the GARCH-based estimates of risk generate the lowest forecast error although these are not necessarily significantly less than those generated by the other techniques considered.     

ERP中固定采购提前期矛盾的一种管理解决方法

采购提前期的确定是确保ERP核心部分MRP有效运行的重要前提之一，也是MRP实施过程中比较棘手的问题。MRP固定提前期的使用，又带来各种问题和矛盾，使得ERP的运行结果无法应用。基于技术的柔性提前期的应用．也因提前期数据难以确定，而不能从根本上解决这些问题。本文作者通过深入研究和实践经历．提出一种通过管理变革的措施，使得这一问题得到有效解决。     

基于SDH的MSTP中的数据交换芯片设计

针对在SDH上构建多业务传输平台(MSTP)的需求,介绍了一种能在SDH传输和接入设备中提供灵活的数据业务支持的PoS(PacketoverSDH)数据交换芯片的设计方法。该芯片基于共享存储器的交换结构,采用了先进的流水线设计方式以提高整体交换容量。芯片支持服务质量保证(QoS)和组播(Multicast)功能,可用于SDH传输和接入设备中,为SDH上的数据业务支持提供简单而高效的解决方案。     

Not enough money or not enough time to satisfy the Thrifty Food Plan? A cost difference approach for estimating a money–time threshold

Food production at home requires money and time. Food assistance programs focus exclusively on the money cost, while ignoring the time cost. This one-dimensional focus could undermine the effectiveness of food assistance programs. In the spirit of Vickery (1977), this paper uses a cost difference approach to develop a money–time threshold, and several related metrics, to determine whether money or time is the most limiting resource in reaching the Thrifty Food Plan (TFP) target. In our empirical analysis we find that when time is ignored, single headed households spend on average 35% more than required to meet the TFP target. However, when time is included, these households spend on average 40% less than required to meet the TFP target. In addition, we find that when time is ignored, 62% of single headed households on average spend enough money to reach the TFP target, but when time is included, only 13% of single headed households spend enough on average to reach the TFP target. Our empirical results suggest that time is more constraining than money in reaching the TFP target. These results imply that metrics solely focusing on money could severely underestimate the gap between actual expenditures and those required to reach the TFP target.     

Modeling and optimization of a container drayage problem with resource constraints

This paper investigates the problem faced by firms that transport containers by truck in an environment with resource constraints. The considered area is export-dominant. As a result, there are three types of container movements as inbound full, outbound full, and inbound empty movements. Both the time windows at the terminal and at the customers’ places and the operation times are considered. Empty containers are also regarded as separate transportation resources besides trucks. The total operating time including waiting time of all the trucks in operation is minimized. The problem is first formulated as a directed graph and then mathematically modeled based on the graph. It falls into a multiple traveling salesman problem with time windows (m-TSPTW) with resource constraints. An algorithm based on reactive tabu search (RTS) is developed to solve the problem. A number of randomly generated examples indicate that the algorithm can be applied to the real world.     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Pricing of the time-change risks

We develop an equilibrium endowment economy with Epstein-Zin recursive utility and a Lévy time-change subordinator, which represents a clock that connects business and calendar time. Our setup provides a tractable equilibrium framework for pricing non-Gaussian jump-like risks induced by the time-change, with closed-form solutions for asset prices. Persistence of the time-change shocks leads to predictability of consumption and dividends and time-variation in asset prices and risk premia in calendar time. In numerical calibrations, we show that the risk compensation for Lévy risks accounts for about one-third of the overall equity premium.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.