全球生产网络的浮现及其探讨——一个基于全球化的地方发展研究框架

1980年代以后全球化和信息化双重发展的进程中,全球经济生产组织模式发生巨大变迁,亟需新的理论框架予以解释。本文首先介绍全球生产网络研究框架的相关理论,重点分析在以劳动分工为元理论的研究体系中,这些理论与全球生产网络研究的承接关系;其次,探讨了经济地理学派与管理学派在研究路径与主要研究内容两个方面的差异;再次,以曼彻斯特经济地理学派相关研究为理论基础,总结出全球生产网络研究框架的主要内容:(1)价值生产与分配;(2)权力组织与网络;(3)地方镶嵌与网络镶嵌。最后提出在中国开展全球生产网络研究具有重要的理论与实践意义。     

第三产业产值比重预测的BP神经网络模型

宏观经济政策的制定要参照各次产业的发展水平,所以对产业发展水平进行评估和预测就显得十分必要。通过运用改进BP算法神经网络的建模方法对我国第三产业产值比重进行分析,最后建立了单隐层的BP神经网络模型。结果表明基于改进BP算法的神经网络模型预测精度高,收敛速度快,具有良好的泛化能力,模型的通用性和实用性强。     

Validation and forecasting accuracy in models of climate change

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.     

Forecasting the NN5 time series with hybrid models

We propose a simple way of predicting time series with recurring seasonal periods. Missing values of the time series are estimated and interpolated in a preprocessing step. We combine several forecasting methods by taking the weighted mean of forecasts that were generated with time-domain models which were validated on left-out parts of the time series. The hybrid model is a combination of a neural network ensemble, an ensemble of nearest trajectory models and a model for the 7-day cycle. We apply this approach to the NN5 time series competition data set.     

ARIMA融合神经网络的人民币汇率预测模型研究

本文在深入分析了单整自回归移动平均(ARIMA)模型与神经网络(NN)模型特点的基础上,建立了ARIMA融合NN的人民币汇率时间序列预测模型。其基本思想是充分发挥两种模型在线性空间和非线性空间的预测优势,即将汇率时间序列的数据结构分解为线性自相关主体和非线性残差两部分,首先用ARI-MA模型预测序列的线性主体,然后用NN模型对其非线性残差进行估计,最终合成为整个序列的预测结果。通过对三种人民币汇率序列的仿真实验表明,融合模型的预测准确率显著高于包括随机游走模型在内的单一模型的预测准确率,从而证实了融合模型用于汇率预测的有效性。这一结果也表明,人民币汇率市场并不符合有效市场假设,可以通过模型对汇率未来走势做出较准确预测。     

基于广义回归神经网络的铁路货运量预测

针对BP神经网络预测存在局部极小缺陷和收敛速度慢的问题,提出基于广义回归神经网络(GRNN)的预测模型。基于我国1999—2008年铁路货运量的历史统计数据,应用GRNN模型和混沌BP神经网络模型对铁路货运量进行预测。通过两种预测模型的计算结果比较说明,GRNN模型具有良好的收敛性和较高的精度,而且模型结构简单、计算速度快,具有良好的实用性。     

Integrating frequent flyer programs in multilateral airline alliances

Forming branded alliances has become a common strategy in the airline industry. These can be defined as ‘multilateral alliances’ as opposed to bilateral alliances. Limited attention has been paid to assessing the impact of multilateral alliances on complex sub-networks, such as distribution networks, cargo alliances and frequent flyer programs. These sub-networks are ‘derivative networks’ within multilateral alliances. These derivative networks create several dilemmas as they evolve causing airline managers to confront fundamental questions regarding harmonization, conflict resolution, and organizational structure.     

STRATEGIC R&D AND NETWORK COMPATIBILITY

We analyse the effects of network externalities in strategic R&D competition. We present a model of two firms competing with R&D investments and prices in a differentiated consumer market. Buyers form firm-specific networks which can be compatible. A high degree of compatibility and large spillovers moderate price competition due to weak strategic value of firm-specific networks and R&D investments, respectively. Asymmetry in product qualities brings out network effects that cancel out in conventional symmetric settings. The lower quality firm increases R&D and decreases its price as spillovers or network compatibility is increased. This happens when R&D and firm-specific network size have high strategic value.     

The baby group: charting the typologies of a parental community of practice.

ABSTRACT

This paper presents an initial exploration of the ‘baby group’. These informal associations of parents (typically mothers) and their children function as important sites of support and friendship. As peer-constituted and mediated ‘communities of practice’, ‘baby’ or ‘mother’s groups’ present as significant forums for negotiating prevailing knowledge-structures that circulate around parenting. In making a case for the baby-group as a social learning system and expression of a core discussion group, this paper will chart a typology of the baby group via an analysis of, firstly, the scholarly and grey-literature attached to baby groups and secondly, an initial incursion into focus group and interview-derived data collected during discussions with one such group located in south-east Queensland, Australia. These two sources provide the foundation from which this paper will offer initial insights into the role and purpose of the baby group, and from which an agenda for the further exploration of the baby group might commence.     

A comparison of statistical tests for the adequacy of a neural network regression model

An integral part of econometric practice is to test the adequacy of model specifications. If a model is adequately specified, it should not leave interesting features of the data-generating process in the errors. Despite the common tradition, the importance of diagnostic checking as a safeguard against mis-specification has only recently been recognized by neural network (NN) practitioners, possibly because this type of semi-parametric methodology was not originally designed for economic and financial applications. The purpose of this paper is to compare a number of analytical statistical testing procedures suitable to diagnostic checking on a neural network regression model. We present the standard Lagrange multiplier (LM) testing framework designed under the assumption of identically distributed disturbances and also examine two modifications that are robust to heteroskedasticity in errors. One modification also gives the researcher an opportunity to incorporate information concerning the volatility structure of the data-generating process in the testing procedure. By means of a Monte Carlo simulation, we investigate the performance of these tests under GARCH-type heteroskedasticity in errors and various distributional assumptions. The results show that although the primary concern of the researcher may be to design a regression model that accurately captures relations in the mean of the conditional distribution, developing a good approximation of the underlying volatility structure generally increases the efficiency of tests in detecting non-adequacy of a NN model.