Retrogressive regime drift within a theory of emergent order

This paper explores the robustness of systems of political economy from within an emergent perspective. Robustness is a quality of the nexus of relationships that emerge within a political economy, and particular attention is given to processes of regime drift that might generate retrogression within a political economy. The central claim advanced here is that robustness is facilitated through polycentric organizational arrangements that entail high degeneracy, while fragility and the emergence of decadence is facilitated in relatively hierarchical arrangements that possess low degeneracy. JEL Code D02, D78, P16, P48, Z13     

Converting social conflict: Focal points and the evolution of cooperation

We examine the ability of focal points to transform situations of potential conflict into situations of cooperation. In performing this function, focal points convert “worst-case scenarios” into “better-case scenarios,” which are easier for political economic systems to handle. Focal points thus contribute to the ability of political economies to perform well in the face of less than ideal conditions, enhancing systemic robustness. JEL Code P0     

Robustness of normal theory statistics in structural equation models*

A condition is given by which optimal normal theory methods, such as the maximum likelihood methods, are robust against violation of the normality assumption in a general linear structural equation model. Specifically, the estimators and the goodness of fit test are robust. The estimator is efficient within some defined class, and its standard errors can be obtained by a correction formula applied to the inverse of the information matrix. Some special models, like the factor analysis model and path models, are discussed in more detail. A method for evaluating the robustness condition is given.     

基于预测能力的扩展极限边界分析法

本文在传统EBA方法的基础上,将其引入到时间序列中,构建以预测为导向的AEBA模型选择方法。AEBA在模型选择上更注重于模型的预测能力,在稳健性检验上细分为模型稳健性检验与参数稳健性检验两部分,提出了基于时间序列预测能力的检验方法。最后实证示例用AEBA方法对影响石油股票指数收益率的因素进行了研究,表明该方法选择的模型的预测能力,特别是短期预测能力要显著强于CAPM、三因子模型、ARMA以及VAR。     

Understanding the drivers of Urban Development Agreements with the rough set approach and robust decision rules

The rise of neoliberalism in the context of urban development has encouraged cooperation between public and private parties. This cooperation is structured by contracts, generally called Urban Development Agreements (UDAs). Being part of the urban regeneration strategies, UDAs aim at achieving durable improvements of abandoned areas, namely brownfields, according to the sustainability principles. Thus, within the negotiation between private developers and public administrations, multiple and conflicting instances have to be faced case by case. Despite the uniqueness of each UDA, it is possible to define a set of pertinent characteristics that play a crucial role in determining the fairness and appropriateness of the public-private partnership. Given this context, we propose a novel variant of the Dominance Rough Set Approach (DRSA) for i) exploring the relationship between condition attributes or criteria and effects of urban development processes and for ii) supporting negotiations according to the detection of a set of relevant features. Specifically, DRSA is applied on a sample of UDAs recently concluded in the Lombardy Region (Northern Italy), and then tested on a sample of other UDAs under the negotiation phase. The analysis involves five dimensions represented by attributes and criteria related to urban, institutional, negotiation, development and economic contexts. The inferred decision rules provide useful knowledge for supporting complex decision processes such as the allocation of costs and benefits within UDAs.     

Robustness and the general dynamic factor model with infinite-dimensional space: Identification,estimation, and forecasting

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. As second-order models, however, they are sensitive to the presence of outliers—an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al. 2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation, and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical data set of 115 US macroeconomic and financial time series.     

基于人工智能的设备颜色特性化方法鲁棒性对比研究

设备颜色特性化是色彩管理技术得以顺利实现的关键技术之一,其核心是设备相关颜色空间和与设备无关颜色空间之间的相互转换。本研究首先定义颜色空间转换方法的鲁棒性概念及其评价方法,在此基础上,对基于模糊控制的颜色空间转换方法、基于动态子空间划分的BP神经网络颜色空间转换方法和基于模糊神经辨识的颜色空间转换方法等基于人工智能的颜色空间转换方法的鲁棒性做了比较研究。研究结果显示：基于模糊神经辨识的颜色空间转换方法能够结合BP神经网络和模糊控制的特点,使其鲁棒性得到很大的提高。     

地方政府帮助地方银行改善经营绩效了吗?--基于“地方保护”和“行政干预”的视角

本文检验了地方政府行为对地方银行经营绩效的影响,发现:(1)"地方保护"具有"保护之手"作用,"行政干预"具有"良性引导"作用,二者有利于降低地方银行的盈利波动性、提升地方银行的运营稳健性。(2)对于资产规模较小的银行、未设立省外分行的银行,"地方保护"和"行政干预"的积极影响较大;对于高技术产业投资比较活跃的地区,"地方保护"和"行政干预"的积极影响较大。(3)当省内经济增长率领先于全国水平时,"地方保护"和"行政干预"依然发挥积极作用;然而,当省内经济增长率落后于全国水平时,"地方保护"会产生"约束之手"作用,"行政干预"会产生"恶性引导"作用,二者反而会加剧地方银行的盈利波动性、降低地方银行的运营稳健性。     

Small sample corrections for LTS and MCD

The least trimmed squares estimator and the minimum covariance determinant estimator [6] are frequently used robust estimators of regression and of location and scatter. Consistency factors can be computed for both methods to make the estimators consistent at the normal model. However, for small data sets these factors do not make the estimator unbiased. Based on simulation studies we therefore construct formulas which allow us to compute small sample correction factors for all sample sizes and dimensions without having to carry out any new simulations. We give some examples to illustrate the effect of the correction factor.