通胀预期与货币需求:实际调整与名义调整机制检验

货币存量调整机制一般可分为两类设定方式:实际调整过程和名义调整过程.在实际调整机制下,预期通胀对货币需求没有独立的影响,而在名义调整机制下,通胀预期自然成为货币需求的一个解释变量.通胀预期在货币需求函数中显著可能并不意味着通胀预期直接影响了货币需求,而是表明了部分调整机制的误设.本文根据预期通胀率在货币需求函数中的显著性,比较了货币持有量名义调整和实际调整机制假说对我国的解释能力,认为名义机制比实际机制更好地描述了中国的经验.我们在一个状态空间中联合估计预期通胀以及货币需求方程,避免了传统"两步法"的不足.     

Developing land use and transport PSS: Meaningful information through a dialogue between modelers and planners

One of the key barriers to integration of land use and transport planning is the lack of a “common language” (i.e. tools, instruments, indicators) that can support planners from both domains in developing shared visions and integrated strategies. Many of such tools and indicators have been developed in recent years, but not so many are implemented in practice. In this paper a new, participatory development approach for planning support systems (PSS) is proposed, termed “mediated planning support” (MPS) that addresses bottlenecks blocking this implementation. It is founded on insights from knowledge management, system dynamics and software innovation and is applied in the Greater Region of Amsterdam. This paper discusses the evolution of the PSS, highlighting the most useful elements which can be applied in other land use and transport planning projects. It offers insights for practitioners and researchers interested in land use and transport planning integration and for professionals that are dealing with supporting planning with information and technologies.     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

Option pricing based on hybrid GARCH-type models with improved ensemble empirical mode decomposition

The exploration of option pricing is of great significance to risk management and investments. One important challenge to existing research is how to describe the underlying asset price process and fluctuation features accurately. Considering the benefits of ensemble empirical mode decomposition (EEMD) in depicting the fluctuation features of financial time series, we construct an option pricing model based on the new hybrid generalized autoregressive conditional heteroskedastic (hybrid GARCH)-type functions with improved EEMD by decomposing the original return series into the high frequency, low frequency and trend terms. Using the locally risk-neutral valuation relationship (LRNVR), we obtain an equivalent martingale measure and option prices with different maturities based on Monte Carlo simulations. The empirical results indicate that this novel model can substantially capture volatility features and it performs much better than the M-GARCH and Black–Scholes models. In particular, the decomposition is consistently helpful in reducing option pricing errors, thereby proving the innovativeness and effectiveness of the hybrid GARCH option pricing model.     

Nowcasting China’s GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated. Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises. In this study, we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data. We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors. We demonstrate the validity of our approach through simulation studies, and explore the applicability of our approach through an empirical study in nowcasting the China’s GDP using 117 monthly data series of several categories in the Chinese market. The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China’s GDP.     

高速铁路动车组检修资源布局优化研究

高速铁路网的日趋完善,高速列车开行数量的进一步增加,对高速铁路动车组检修资源布局提出新的要求。为合理优化动车组检修资源布局,提高动车组检修效率,在分析我国动车段和动车运用所管理模式及布局方案的基础上,将动车组检修资源布局优化问题转化为基于多基地车辆路径运用的基地选址问题,基于"备选运用所-运输需求-动车组运用"间的关联关系构建动车组运用检修接续网络,建立动车组检修资源布局优化模型。以某区域的铁路网为例,利用近远期规划优化动车组检修资源布局,验证模型有效性,为今后动车组检修资源布局优化提供参考。     

江西省居家养老改进模式研究

江西省已在2005年进入人口老龄化社会。江西省统计局人口变动情况抽样调查资料显示，在省内居民生活水平上升、平均预期寿命提高，以及人口生育率持续保持较低水平等因素的综合作用下，江西省人口老龄化进程持续加快。妥善解决老年人的需求，将成为实现江西崛起的关键一步。文中通过对江西省内实地调研，针对现有居家养老模式存在的缺陷，提出合理建议。     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

Estimation of zero-intelligence models by L1 data

A unit volume zero-intelligence (ZI) model is defined and the distribution of its L1 process is recursively described. Further, a generalized ZI model allowing non-unit market orders, shifts of quotes and general in-spread events is proposed and a formula for the conditional distribution of its quotes is given, together with a formula for price impact. For both the models, MLE estimators are formulated and shown to be consistent and asymptotically normal. Consequently, the estimators are applied to data of six US stocks from nine electronic markets. It is found that more complex variants of the models, despite being significant, do not give considerably better predictions than their simple versions with constant intensities.     

Scaling the volatility of GDP growth rates

The distribution of shocks to GDP growth rates is found to be exponential rather than normal. Their standard deviation scales with GDP^β where β=−0.15±0.03. These macroeconomic results place restrictions on the microeconomic structure of interactions between agents.