Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

基于递归对角神经网络自适应均衡算法的研究

阐述了神经网络自适应均衡算法的基本原理,提出了一种基于递归对角神经网络的自适应均衡算法,推导了算法的迭代公式,进行了计算机仿真。结果表明,随着传输信号信噪比的增加,稳态剩余误差减小,误码率降低。     

投入产出乘数模型与其扩展方法

在投入产出模型里引入凯恩斯乘数理论，建立能够计算结构化乘数的投入产出乘数模型，是近年来投入产出领域探讨较多的一个问题。从前提假设、乘数效应的传导机制和最终推导结果等方面对凯恩斯乘数模型和投入产出乘数模型进行比较，指出建立投入产出乘数模型将凯恩斯乘数进行结构化处理，应该从放宽前提假设条件入手，并以引入列向旁侧效应为例对原模型进行了扩展。     

中国通货膨胀成因的实证分析

通过对可能影响我国通货膨胀的因素,包括经济增长、货币供应量、居民消费水平和工资的格兰杰因果分析和自回归分布滞后模型的拟合,可知我国通货膨胀和货币供应量、居民预期有密切关系,而和其他因素没有显著关系.     

A resilience-based framework for evaluating adaptive co-management: Linking ecology, economics and society in a complex world

Adaptive co-management brings together collaborative and adaptive approaches in pursuit of sustainable resource use and social-ecological resilience. Enthusiasm for this management approach, however, is countered by recent critiques regarding outcomes. A lack of evidence from consistent evaluation of adaptive co-management further exacerbates this situation. This paper revisits the issue of evaluation in natural resource management and recasts it in light of complex adaptive systems thinking. An evaluative framework for adaptive co-management is developed which directs attention toward three broad components: ecosystem conditions, livelihood outcomes and process and institutional conditions. Scale-specific parameters are offered for each component to facilitate systematic learning from experience and encourage cross-site comparisons. Conclusions highlight the importance of systematically incorporating evaluation into the adaptive co-management process and recognize the challenge for resource agencies and researchers to shift from a conventional to a complex adaptive system perspective.     

基于复杂性科学观的区域创新网络研究

复杂性科学打破了新古典经济学的简单性、稳定性和线性的均衡范式,开创了一种建立在复杂性、动态性和非线性基础上的演化范式。区域创新网络由于具有非线性、动态性和不可逆性等特征,更适合纳入到复杂性科学的范式中来研究。根据区域创新网络的复杂性特征,归纳出其实质上是一种复杂性适应系统,进而运用复杂性科学对其进行了阐释,最后得出了基于高新区培育和发展区域创新网络的几点启示。     

用单片机实现渗碳工艺的控制

本文介绍了一个简单、廉价的单片机系统,并用其实现了渗碳过程的各段时间和渗剂滴量的自动控制,取得了良好的效果。     

Ad hoc网络中基于多波束天线的集中式低时延调度算法

UxDMA算法是一种高效的集中式算法，是用于时分多址、频分多址和码分多址信道分配的统一算法。在UxDMA的基础上，利用多波束天线的多波束形成能力，针对低时延定向ad hoc网络提出了一种集中式调度算法——CLSM(Centralized Low-delay Scheduling Algorithm Based on Multi-beam Antennas)。CLSM通过不同时延等级限制的报文来比较着色后发送链路的优先级，优先选择高优先级链路传输。通过仿真验证了CLSM的性能：与UxDMA相比，该算法在多时延限制的发送端调度中表现出了更好的吞吐量和时延性能。     

Escape dynamics: A continuous-time approximation

We extend a continuous-time approximation approach to the analysis of escape dynamics in economic models with constant gain adaptive learning. This approach is based on the application of the results of continuous-time version of large deviations theory to the linear diffusion approximation of the original discrete-time dynamics under learning. We characterize escape dynamics by analytically deriving the most probable escape point and mean escape time. The approximation is tested on the Phelps problem of a government controlling inflation while adaptively learning a misspecified Phillips curve, studied previously by Sargent (1999) and Cho et al. (2002) (henceforth, CWS), among others. We compare our results with simulations extended to very low values of the constant gain and show that, for the lowest gains, our approach approximates simulations relatively well. We express reservations regarding the applicability of any approach based on large deviations theory to characterizing escape dynamics for economically plausible values of constant gain in the model of CWS when escapes are not rare. We show that for these values of the gain it is possible to derive first passage times for learning dynamics reduced to one dimension without resort to large deviations theory. This procedure delivers mean escape time results that fit the simulations closely. We explain inapplicability of large deviations theory by insufficient averaging near the point of self-confirming equilibrium for relatively large gains which makes escapes relatively frequent, suggest the changes which might help approaches based on the theory to work better in this gain interval, and describe a simple heuristic method for determining the range of constant gain values for which large deviations theory could be applicable.