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.     

Milieu as a pertinent unit of analysis in project marketing

The purpose of this article is to shift gear from the purely economic to the socioeconomic level in project marketing and consequently to demonstrate that project marketing consists, to a certain extent, in the management of a firm's relationships to a local network of business and non-business actors, named the milieu. This networking action forms the basis of a proactive approach aiming at anticipating bids and at maximizing the firm's chances of success on on-going projects.     

Networks or neighborhoods? Correlations in the use of publicly-funded maternity care in California

This study focuses on “network effects” in the utilization of publicly-funded prenatal care using Vital Statistics data from California for 1989-2000. Networks are defined using 5-digit zip codes and a woman's racial or ethnic group. Like others, we find evidence that the use of public programs is highly correlated within groups defined using race/ethnicity and neighborhoods. These correlations persist even when we control for many unobserved characteristics by including zip code-year fixed effects, and when we focus on the interaction between own group behavior and measures of the potential for contacts with other members of the group (“contact availability”). However, the richness of our data allows us to go further and to conduct several tests of one important hypothesis about networks: that the estimated effects represent information sharing within groups. The results cast doubt on the idea that the observed correlations can be interpreted as evidence of information sharing. In particular, we find estimated effects to be as large or larger among women who have previously used the program as among first-time users.     

BP神经网络在供应链绩效评价中的应用

首先区分不同主体,权衡成本与收益分层次选择指标,基于流程构建一个供应链绩效评价的指标体系;然后介绍了BP神经网络原理及模型,最后探讨了BP神经网络在供应链绩效评价中的应用。     

Investigating the accuracy of cross-learning time series forecasting methods

The M4 competition identified innovative forecasting methods, advancing the theory and practice of forecasting. One of the most promising innovations of M4 was the utilization of cross-learning approaches that allow models to learn from multiple series how to accurately predict individual ones. In this paper, we investigate the potential of cross-learning by developing various neural network models that adopt such an approach, and we compare their accuracy to that of traditional models that are trained in a series-by-series fashion. Our empirical evaluation, which is based on the M4 monthly data, confirms that cross-learning is a promising alternative to traditional forecasting, at least when appropriate strategies for extracting information from large, diverse time series data sets are considered. Ways of combining traditional with cross-learning methods are also examined in order to initiate further research in the field.     

基于人工神经网络第三方物流企业的物流中心选址研究

建立了第三方物流企业的物流中心选址评价指标体系，探讨并应用人工神经网络解决第三方物流企业的物流中心选址评价问题，并给出了神经网络的评价模型。实例证明，该方法是可行的。