核心企业网络能力、创新网络与科创型特色小镇发展——以杭州云栖小镇为例

科创型特色小镇作为我国推行创新驱动发展战略的重要部署，对当地创新创业发挥着重要作用。拥有核心企业主导的创新网络的科创型特色小镇，具有更强的发展动力。因此，探索核心企业网络能力、创新网络与科创型特色小镇发展之间的关系具有重大意义。运用案例研究方法，以杭州云栖小镇为例展开研究，结果发现：①创新网络演化阶段可以划分为差距清晰化阶段、提升探索阶段和成熟规范化阶段；②核心企业网络能力对创新网络演化发展具有促进作用，并且在创新网络演化不同阶段存在差异性；③核心企业网络能力、创新网络和科创型特色小镇产业发展存在协同演化。     

Top management team’s formal network and international expansion of Chinese firms: The moderating role of state ownership and political ties

This article explores the role of the formal network centrality of top management teams (TMT) for foreign expansion, looking at the case of Chinese firms. The former is defined by the degree to which top managers are connected with TMTs of other firms in formal ways, through service as independent board members. We explore boundary conditions, comparing state ownership with political ties. The analysis of a panel data of 489 firms expanding to 72 developed and developing host markets in the period 2000–2012 confirms that network centrality facilitates internationalization. We found that TMT network centrality had a stronger effect on internationalization in developed than emerging markets. Conversely, state ownership had a positive moderating effect in the latter and political ties a negative effect in developed ones. The literature on comparative institutional analysis suggests that formal ties are more important in developed economies, and informal ties in emerging ones. However, formal political ties and/or links to the Chinese state may be more of value in internationalizing into other emerging markets, where the balance of diplomatic power may be more skewed in China’s favor.     

Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana

The major challenge in managing blood products lies in the uncertainty of blood demand and supply, with a trade-off between shortage and wastage, especially in most developing countries. Thus, reliable demand predictions can be imperative in planning voluntary blood donation campaigns and improving blood availability within Ghana hospitals. However, most historical datasets on blood demand in Ghana are predominantly contaminated with missing values and outliers due to improper database management systems. Consequently, time-series prediction can be challenging since data cleaning can affect models’ predictive power. Also, machine learning (ML) models’ predictive power for backcasting past years’ lost data is understudied compared to their forecasting abilities. This study thus aims to compare K-Nearest Neighbour regression (KNN), Generalised Regression Neural Network (GRNN), Neural Network Auto-regressive (NNAR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) models via a rolling-origin strategy, for forecasting and backcasting a blood demand data with missing values and outliers from a government hospital in Ghana. KNN performed well in forecasting blood demand (12.55% error); whereas, ELM achieved the highest backcasting power (19.36% error). Future studies can also employ ML algorithms as a good alternative for backcasting past values of time-series data that are time-reversible.     

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.     

基于IPSO-BP神经网络的铁路客运量预测

在分析有关铁路客运量预测方法的基础上,针对BP神经网络模型存在的不足,提出基于粒子群优化算法(PSO)优化BP神经网络的参数,即改进的PSO方法(IPSO)。以我国1990—2007年的铁路客运量为研究对象,确定输入样本和输出样本,以及训练集和测试集,建立基于IPSO的BP神经网络优化模型预测铁路客运量。预测结果表明,IPSO-BP网络的算法训练时间短,收敛速度快,预测精度高。     

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。     

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.     

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。     

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.     

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.