Stress testing network reconstruction via graphical causal model

An resilience optimal evaluation of financial portfolios implies having plausible hypotheses about the multiple interconnections between the macroeconomic variables and the risk parameters. In this article, we propose a graphical model for the reconstruction of the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, it is this structure that we call stress testing network. In this model, the relationships between the macroeconomic variables and the risk parameter define a “relational graph” among their time‐series, where related time‐series are connected by an edge. Our proposal is based on the temporal causal models, but unlike, we incorporate specific conditions in the structure which correspond to intrinsic characteristics this type of networks. Using the proposed model and given the high‐dimensional nature of the problem, we used regularization methods to efficiently detect causality in the time‐series and reconstruct the underlying causal structure. In addition, we illustrate the use of model in credit risk data of a portfolio. Finally, we discuss its uses and practical benefits in stress testing.     

A new nonparametric stability test with an application to major Chinese macroeconomic time series

In this paper, we propose a new test for testing the stability in macroeconomic time series, based on the LASSO variable selection approach and nonparametric estimation of a time-varying model. The wild bootstrap is employed to obtain its data-dependent critical values. We apply the new method to test the stability of bivariate relations among 92 major Chinese macroeconomic time series. We find that more than 70% bivariate relations are significantly unstable.     

基于自回归XGBoost时序模型的GDP预测实证

GDP是反映一个国家国民收入、居民消费能力和经济发展的重要宏观经济指标,也是制定相关经济政策的重要依据.选择合适的统计方法研究GDP的发展变化规律,进行短期的高精度预测,对我国的宏观经济决策具有重要意义.研究选用基于自回归的XGBoost时序模型对我国1978-2018年GDP进行拟合预测,Rstudio软件运行结果显示,XGBoost时序模型比经典的时间序列预测模型ARIMA模型、BP神经网络模型、贝叶斯时序模型具有更高的预测精度.在此基础上,运用XGBoost时序模型对我国2019-2023年的GDP进行短期预测,研究结果显示,未来5年我国GDP依然保持持续稳定增长趋势.     

基于景气跟踪图的经济景气分析方法

首次将景气跟踪图方法引入中国经济景气分析中,并从宏观经济和行业分析两个维度对中国经济景气情况进行了分析.景气跟踪图方法基于时钟分析方法,对多个重要的经济时间序列指标的发展、运行进行跟踪,进而分析国民经济运行状态.景气跟踪图方法是一种新的景气分析方法,目前多用于宏观经济分析,作者尝试将其引入到行业分析中.针对直接采用原有景气跟踪图可能存在的问题,作者结合先行指标体系进行景气跟踪图分析,同时提出了基于景气跟踪图的经济持久期分析方法.中国经济的实证结果表明改进的景气跟踪图方法在宏观经济与行业分析中均有非常良好的表现,是现有景气分析方法的很好改进与补充.     

Retrieving seasonally adjusted quarterly growth rates from annual growth rates that are reported quarterly

Most macroeconomic time series are reported on a quarterly basis. However, in many cases, official statistical agencies report annual (or ‘year-on-year’) growth rates rather than the quarterly data themselves. In this paper, we demonstrate how to recover seasonally adjusted quarterly growth rates from annual growth rates that are reported quarterly. As an illustration, we apply the technique to the highly seasonal UK Retail Sales series. Using only the annual growth rates calculated from the underlying data, we find that our procedure generates estimated seasonally adjusted quarterly growth rates that are almost indistinguishable from the ‘true’ seasonally adjusted quarterly growth rates.     

江苏省区域经济发展水平实证分析——时序多指标综合评价方法及应用

区域经济发展水平是政策制定和管理决策的基础。不同于通常的静态评价方法,本文采用了一种时序多指标的系统工程评价方法,首先对时序多指标数据进行标准化处理,接着求出评价指标和评价年份各自的加权系数,然后确定出各年度的和综合的经济效益评价值,据此进行了聚类分析,将江苏省13个地区分成五类,最后根据评价结果提出一些政策建议。     

专利申请与授权量的时间序列分析

专利的申请和授权量动态分析与预测是建立专利预警机制、设计与制定相关政策和战略的基础,具有重要的理论意义。起伏型时间序列法是一种新的时间序列分析法,提出用起伏型时间序列法对专利申请与授权数据进行动态分析。以2005年国内专利申请与授权数量月动态为研究数据,对专利申请、授权及发明专利申请数量的月动态进行建模模拟,结果令人满意,说明起伏型时间序列分析方法可应用于专利申请与授权动态模拟,从而丰富了专利申请与授权数据动态分析方法。     

Monetary and Fiscal Policy Interaction in the EMU: A Dynamic Game Approach

The interaction of monetary and fiscal policies is a crucial issue in a highly integrated economic area as the European Union. We investigate to which extent the EMU, that introduced a common monetary policy and restrictions on fiscal policy at the national level, benefits from macroeconomic policy cooperation due to the various interactions, spillovers and externalities from national macroeconomic policies. To study the effects of policy cooperation we compare the impact of three alternative policy regimes in a stylized dynamic model of the EMU: (i) non-cooperative monetary and fiscal policies, (ii) partial cooperation, and (iii) full cooperation both in symmetric and asymmetric settings where countries differ in structural characteristics, policy preferences and/or bargaining power. The paper introduces an analysis of coalitional behaviour in a dynamic setting into the literature.     

上市公司经营业绩的时序多指标综合评价

本文首先指出了传统的静态多指标综合评价的缺陷,在考虑上市公司静态业绩评价和评价值增长变化两方面的基础上,提出了应用主成分分析和理想点法等构造的时序多指标综合评价方法,最后通过电力类上市公司的动态综合评价说明了该方法确实是一种有效的动态评价方法。     

基于CGE模型的我国西北地区民族经济政策模拟和实证研究

民族经济政策的模拟评估方法的缺失一直以来是我国民族经济政策效果有效性验证的一个短板。本文构建了基于CGE的西北地区民族经济政策评估模型,针对一般转移性支付政策进行系统模拟、政策力度对西北地区的经济要素以及当地企业产出的影响程度纵向对比分析,以及与东部沿海地区横向对比分析。本文的政策评估建模和模拟分析可以为我国西部大开发战略中一般性转移支付政策提供相应的决策支持。     

改进的基于特征向量集的距离判别及在企业信用评价中的应用

在基于特征向量集的距离判别的基础上,提出新的判别分析方法,试图解决现有判别分析方法中计算量大及对复杂数据判别效果差的缺点.同时,将方法用于企业信用评价中,并与传统的判别方法及一些改进的判别方法作比较,实验结果表明,方法提高了企业信用评价的准确率.     

An application of DPCA to oil data for CBM modeling

In multivariate time series analysis, dynamic principal component analysis (DPCA) is an effective method for dimensionality reduction. DPCA is an extension of the original PCA method which can be applied to an autocorrelated dynamic process. In this paper, we apply DPCA to a set of real oil data and use the principal components as covariates in condition-based maintenance (CBM) modeling. The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2). It is shown that the average maintenance cost corresponding to the optimal policy for Model 1 is considerably lower than that for Model 2, and when the optimal policies are applied to the oil data histories, the policy for Model 1 correctly indicates almost twice as many impending system failures as the policy for Model 2.     

Nonparametric time series prediction: A semi-functional partial linear modeling

There is a recent interest in developing new statistical methods to predict time series by taking into account a continuous set of past values as predictors. In this functional time series prediction approach, we propose a functional version of the partial linear model that allows both to consider additional covariates and to use a continuous path in the past to predict future values of the process. The aim of this paper is to present this model, to construct some estimates and to look at their properties both from a theoretical point of view by means of asymptotic results and from a practical perspective by treating some real data sets. Although the literature on the use of parametric or nonparametric functional modeling is growing, as far as we know, this is the first paper on semiparametric functional modeling for the prediction of time series.     

An Econometric Approach to Short-Run Analysis of the U.K. Economy, 1955-66

The structural approach to economic forecasting differs from familiar models based on the classical analysis of time series in that it requires the specification and estimation of the inter-relationships that are taken to represent economic structure. The first part of the paper deals briefly and rapidly with some of the problems encountered in specifying and estimating such structural models. The second part deals with the applications of such models and their implications for forecasting and policy analysis. These sections are intended to be fairly allusive and preparatory to the main body of the paper which discusses a specific example of a simple econometric model of the U.K. economy constructed at the London Business School. The model is described in terms of its structural relationships, and the results of some simulation work are presented. The paper concludes with some general evaluation of the approach in the light of the specific illustration discussed.     

宏观调控政策协调的TU和NTU合作博弈分析

宏观经济政策之间的协调及其调控收益的分配实际上可看做n人合作博弈.通过构造宏观政策组合的二人TU博弈模型,分析了政策组合的调控力度、收益及其分配比例值的相互关系,并重新构建政策组合调控总收益的特征函数,把宏观政策协调的二人TU博弈分析依次扩展至n人TU博弈情形和NTU博弈情形,给出相应的政策调控收益分配的Shapley值,提出确定宏观政策组合中各项政策调控的参与力度的具体办法,有效地解释了加强政策协调配合的宏观调控精神,对宏观调控实践具有一定的指导意义.     

Network centrality and multiscale transition asymmetry in the heart rate variability analysis of normal and preeclamptic pregnancies

In the present work we define and apply a new methodology for the analysis of time series that involves a network representation. In this methodology, time series are transformed into a network with the subsequent calculation of some centrality indexes as well as the asymmetry of the transition frequency matrix, across different scales. The properties of the proposed indexes were analyzed with short-length simulated and physiological time series. In this case, we use RR time intervals, obtained from electrocardiographic records. The proposed indexes are compared with common complexity indexes in the analysis of the RR time series in normal and preeclamptic pregnancies.     

News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions

The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive frameworks base their forecasts primarily on time series analysis, as well as the judgments of experts. As a consequence, current approaches are often biased and prone to error. In order to reduce forecast errors, this paper presents an innovative methodology that extends lag variables with unstructured data in the form of financial news: (1) we apply a variety of models from machine learning to word counts as a high-dimensional input. However, this approach suffers from low interpretability and overfitting, motivating the following remedies. (2) We follow the intuition that the economic climate is driven by general sentiments and suggest a projection of words onto latent semantic structures as a means of feature engineering. (3) We propose a semantic path model, together with estimation technique based on regularization, in order to yield full interpretability of the forecasts. We demonstrate the predictive performance of our approach by utilizing 80,813 ad hoc announcements in order to make long-term forecasts of up to 24 months ahead regarding key macroeconomic indicators. Back-testing reveals a considerable reduction in forecast errors.     

基于贝叶斯因素-时间序列模型的罪犯地理特征仿真研究

分析了可能影响犯罪地点的一些因素,并结合当前热点问题贝叶斯概率模型建立了贝叶斯因素分析模型.通过实例分析发现这种模型比较适合于较少案例情况下,当案件数小于五种时也有一定的预测效果.之后又建立了一种时间序列分析模型,这种模型可以对两个住所的情况和罪犯改变其犯罪策略的情况进行分析,实验发现当案例数较多时,这种模型表现出了很高的准确性.用蒙特卡洛方法生成的数据来测试模型的相关准确性、效率、可信度等数据,并探讨每个模型的优缺点为综合性地运用与实践提供前瞻性的理论依据.     

有序判别分析新算法及其应用

判别分析是用已知分类数据建模对未知分类数据进行判别的方法,所用数据和分类不分顺序。要对有序又有周期数据进行判别分析,就要探索有序判别的新方法。这种方法的分类应当是有序的,并且能够排除事物发展周期性的干扰。本文介绍多元数据有序判别分析新方法的原理、建模流程、应用流程和应用实例。这种判别分析将分类建模与判别归类分开。新方法对多元数据建模时在多类模型中建立滑移的多套子模型,应用时根据应用领域的知识对样本归属作初步预估,然后程序选择相关的子模型进行判别归类。这种方法解决了由于时间序列多元数据周期性造成的样本分类颠倒问题,为时间序列数据的分类和预测开辟了新途径,在实际应用中取得了良好的效果,解决了重大难题。     

A least-squares model specification test for a class of dynamic nonlinear economic models with systematically varying parameters

The problem of structural instability in estimated macroeconomic relationships has recently surfaced anew, due to a series of articles by the rational expectations school. A key explanation for the observed instability is said to be that the parameters appearing in modelled macroeconomic relationships, traditionally treated as constant or purely random, must realistically be viewed as the reflections of the demand and supply decisions of optimizing agents in the economy reacting rationally to changes in endogenous and exogenous variables, including changes induced by shifts in government policy decision rules. The present paper develops a least-squares measure for simultaneously testing the basic compatibility of prior dynamical, observational, and distributional model specifications against actual data for a class of dynamic nonlinear economic models with parameters explicitly modelled as nonlinear functions of endogenous and exogenous variables. Using invariant imbedding techniques, an algorithm is derived for sequentially updating the optimal least-squares estimates for parameters, endogenous variables, and squared residual modelling error sums as the duration of the process increases and new observations are obtained.Dedicated to R. BellmanThis work was partially supported by the National Science Foundation under Grant No. ENG-77-28432 and the National Institutes of Health under Grant No. GM-3732-03. A previous version of this paper (Ref. 1) was presented at the Econometrics Modelling Research Seminar, Department of Economics, University of Southern California, Los Angeles, 1979. The authors are grateful to numerous colleagues for helpful comments.