Which bank is the “central” bank?

Liquidity flows through a financial network cannot be accurately described using external processing constraints alone. Behavioral aspects of participants also matter. A method similar to Google's PageRank procedure is used to produce a ranking of participants in the Canadian Large Value Transfer System in terms of their daily liquidity holdings. Accounting for differences in banks’ processing speeds is essential for explaining why observed distributions of liquidity differ from the initial distributions, which are determined by the credit limits selected by banks. Delay tendencies of banks are unobservable in the data and are estimated using a Markov model.     

中小企业创新网络形成与发展的动力机制研究

中小企业在促进经济增长、扩大就业方面发挥着重要的作用,而中小企业创新网络的构建,有助于中小企业在网络中获得技术创新的资源,提高企业的创新绩效。本文首先界定了中小企业创新网络的概念。并进一步具体分析中小企业创新网络的形成与发展的动力机制。     

基于双网络视角的众创空间合作创新网络演化机制研究

众创空间逐渐成为当前研究热点,构建高效的合作创新网络可以提升其运营绩效。当前,合作创新网络解耦研究越来越多,对知识网络与合作网络交互关系进行研究能够更加完整地再现真实网络作用机理。基于双网络视角,对知识网络与合作网络进行解耦,探索众创空间运营机制与创业企业合作策略对网络创新绩效的影响,利用数理建模和仿真方法模拟众创空间知识网络与合作网络的动态演化结构以及两种网络间的交互作用,为提升众创空间运营效率提出改进建议。结果表明：企业合作关系断链重连概率对合作创新绩效有较大影响,这一概率存在相对合理区间;知识网络与合作网络结构在双网络结构机制下有较大差异,并且在动态演化过程中存在交互作用。     

Daily retail demand forecasting using machine learning with emphasis on calendric special days

Demand forecasting is an important task for retailers as it is required for various operational decisions. One key challenge is to forecast demand on special days that are subject to vastly different demand patterns than on regular days. We present the case of a bakery chain with an emphasis on special calendar days, for which we address the problem of forecasting the daily demand for different product categories at the store level. Such forecasts are an input for production and ordering decisions. We treat the forecasting problem as a supervised machine learning task and provide an evaluation of different methods, including artificial neural networks and gradient-boosted decision trees. In particular, we outline and discuss the possibility of formulating a classification instead of a regression problem. An empirical comparison with established approaches reveals the superiority of machine learning methods, while classification-based approaches outperform regression-based approaches. We also found that machine learning methods not only provide more accurate forecasts but are also more suitable for applications in a large-scale demand forecasting scenario that often occurs in the retail industry.     

链网互动与区域产业网络的优化

文章在讨论产业网络及其优化内涵的基础上,分析了区域产业网络优化的两种基本模式,即构建产业创新网络和嵌入全球价值链,由于二者分别存在"网络陷阱"和"全球价值链陷阱",通过构建链网互动机制来优化区域产业网络是更为可行的思路,文中构建了一个链网互动机制的概念框架,并结合我国实际提出了促进区域产业网络优化的若干政策建议.     

Religion and economic growth: was Weber right?

Evidence of falling wages in Catholic cities and rising wages in Protestant cities between 1500 and 1750, during the spread of literacy in the vernacular, is inconsistent with most theoretical models of economic growth. In The Protestant Ethic, Weber suggested an alternative explanation based on culture. Here, a theoretical model confirms that a small change in the subjective cost of cooperating with strangers can generate a profound transformation in trading networks. In explaining urban growth in early-modern Europe, specifications compatible with human-capital versions of the neoclassical model and endogenous-growth theory are rejected in favor of a “small-world” formulation based on the Weber thesis.     

基于混沌时间序列的CPI短期预测分析

有效控制CPI关乎国计民生,但其变动的随机性增加了预测难度。在对混沌时间序列预测流程进行了梳理的基础上,首先采用最大Lyapunov指数法辨别CPI时序的混沌特性,运用混沌理论重构相空间,利用逐步回归分析和BP神经网络进行混沌预测,并将ARIMA模型作为比较预测模型,最后从预测和拟合两个方面对模型进行效果评价。综合分析结果显示:2014年CPI增长范围为[2.8%,5.3%],变动幅度较大;2015年将高于4%;而2016年有望突破5%。该研究为CPI短期预测提供了较为可靠的方法,且预测结果可成为政府宏观调控政策的科学依据。     

Measuring network effects on trade: A reexamination

Building upon [Greaney, T.M., 2005. Measuring network effects on trade: are Japanese affiliates distinctive? Journal of the Japanese and International Economies 19, 194–214], this research provides improved estimates of the impact of network effects on trade conducted by foreign affiliates operating in the US. With an expanded and improved data set, I find that both home and regional biases are much stronger for affiliates’ imports than for their exports. At the country-specific level, I find evidence to support the hypothesis that Japanese affiliates have particularly strong network effects, but these effects are limited to a home bias effect alone. Although Japanese affiliates show signs of a regional, or Asian, network effect in their import pattern, the strength of this effect is the weakest among all of the countries tested. Only two countries’ affiliates show signs of regional bias in their export behavior, Australia and the Netherlands.     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates

The present paper examines the out-of-sample forecasting performance of four conditional volatility models applied to the European Monetary System (EMS) exchange rates. In order to provide improved volatility forecasts, the four models’ forecasts are combined through simple averaging, an ordinary least squares model, and an artificial neural network. The results support the EGARCH specification especially after the foreign exchange crisis of August 1993. The superiority of the EGARCH model is consistent with the nature of the EMS as a managed float regime. The ANN model performed better during the August 1993 crisis especially in terms of root mean absolute prediction error.