Predictive financial models of the euro area: A new evaluation test

This paper investigates the predictive ability of financial variables for euro area growth. Our forecasts are built from univariate autoregressive and single equation models. Euro area aggregate forecasts are constructed both by employing aggregate variables and by aggregating country-specific forecasts. The forecast evaluation is based on a recently developed test for equal predictive ability between nested models. Employing a monthly dataset from the period between January 1988 and May 2005 and setting the out-of-sample period to be from 2001 onwards, we find that the single most powerful predictor on a country basis is the stock market returns, followed by money supply growth. However, for the euro area aggregate, the set of most powerful predictors includes interest rate variables as well. The forecasts from pooling individual country models outperform those from the aggregate itself for short run forecasts, while for longer horizons this pattern is reversed. Additional benefits are obtained when combining information from a range of variables or combining model forecasts.     

The Relation between Aggregate Earnings and Security Returns over Long Intervals*

This paper provides a theoretical explanation and consistent empirical evidence for the increase in the contemporaneous correlation between returns and aggregate earnings as the return interval is lengthened. Consistent with intuition and with Easton, Harris, and Ohlson 1992, the analysis shows that aggregation over time renders the lag in accounting recognition relatively less important and thus improves the returns‐earnings R². Interestingly, the analysis also reveals that aggregating earnings over longer periods increases the positive covariance between aggregate earnings and the accounting lag, which may further increase the R². This positive covariance can lead to an earnings coefficient greater than one over some range of aggregation, which is consistent with the findings of Easton et al. that over the 10‐year interval the returns‐earnings regression slope coefficient is greater than one (1.7). The empirical results highlight the fact that the slope coefficient, which is greater than one and increasing with the interval, accounts for much of the increment to the returns‐earnings R². In fact, constraining the slope coefficient to be one results in an R² of 11 percent for the 10‐year interval, which is considerably lower than the R² of 47 percent when the regression is unconstrained. Hence, the positive covariance between current earnings and the accounting lag, rather than the diminishing effect of the accounting lag, appears to be the dominant explanation for the observed high R² over long intervals.     

Agriculture’s Inter‐industry Linkages,Aggregation Bias and Rural Policy Reforms

As agricultural policy reform and its effects have become increasingly territorialised, analyses that attempt to explain or predict impacts need to be both more localised and to identify spill‐over effects. Local and regional general equilibrium approaches have become increasingly popular because they can extend predictions of policy shocks obtainable from partial equilibrium sectoral models to identify the wider effects. However, agriculture is usually described as a single sector in input–output accounts, whereas policy shocks that affect constituent commodities with differential impacts will have inter‐industry effects that are different to those implied by average input–output coefficients. Regionalisation of aggregated input–output tables adds further to these difficulties. The objective of this study is to develop a practical method for dealing with these problems. It describes the theoretical basis of aggregation bias and shows how it can be measured, in two contrasting case study regions in the UK and Sweden. Having established that this is a significant issue, a simple but effective procedure is demonstrated, based on additional information on variable costs, which transforms policy shocks from a direct change in agricultural output to that transmitted to the suppliers of inputs. This method provides an impact close to that which could be calculated if the general equilibrium system had indeed been disaggregated, and supports use of this approach in impact studies where insufficient time or funding are available for complete disaggregation of an agricultural sector’s regional accounts.     

Disaggregation and consolidation of imperfect quality shipments in an extended EPQ model

We consider a standard economic production quantity (EPQ) model. Due to manufacturing variability, a fraction P of the produced inventory will have imperfect quality, where P is a random variable with a known distribution. We consider a 100% inspection policy and further assume that the inspection rate is larger than that of production. Thus, all imperfect quality items will be detected by the end of the production cycle. For such an augmented EPQ model, we first derive the new optimal production quantity assuming that the imperfect quality items are salvaged once at the end of every production cycle. Then, we extend this base model to allow for disaggregating the shipments of imperfect quality items during a single production run. Finally, we consider aggregating (or consolidating) the shipments of imperfect items over multiple production runs. Under both scenarios we derive closed-form expressions for both the economic production quantity and the batching policy, and show that our desegregation/consolidation schemes can lead to significant cost savings over the base model.     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

A practical road map for assessing cyber risk

The increase in interconnectivity and developments in technology have caused cyber security to become a universal concern. This paper highlights the dangers of the evolution of cyber risk, the challenges of quantifying the impact of cyber-attacks and the feasibility of the traditional actuarial methodologies for quantifying cyber losses. In this paper, we present a practical roadmap for assessing cyber risk, a roadmap that emphasizes the importance of developing a company and culture-specific risk and resilience model. We develop a structure for a Bayesian network to model the financial loss as a function of the key drivers of risk and resilience. We use qualitative scorecard assessment to determine the level of cyber risk exposure and evaluate the effectiveness of resilience efforts in the organization. We highlight the importance of capitalizing on the knowledge of experts within the organization and discuss methods for aggregating multiple assessments. From an enterprise risk management perspective, impact on value should be the primary concern of managers. This paper uses a value-centric/reputational approach to risk management rather than a regulatory/capital-centric approach to risk.     

Fixed costs and long-lived investments

Fixed costs models are difficult to analyze because they feature non-degenerate, time-varying distributions of capital across firms. If investments are sufficiently long-lived however then the cross-sectional distribution of capital holdings has virtually no bearing on the equilibrium and the aggregate behavior of fixed-cost models is essentially identical to neoclassical models. The findings are due to a near infinite elasticity of investment timing for long-lived investments – a feature shared by fixed-cost models and neoclassical models. “Irrelevance results” found in numerical studies of fixed-cost models are not parametric special cases but instead are fundamental properties of models with long-lived investment goods.     

基于科技型人才聚集的高等院校协同创新系统建模与仿真

高等院校是推进协同创新的重要力量,相关研究议题日益受到国内外学者关注。在对高等院校协同创新模式进行研究的基础上,以科技型人才聚集为视角,提出高等院校协同创新复杂系统。通过分析协同创新环境与高等院校协同创新系统的作用过程,基于系统动力学方法构建相应模型,运用Vensim PLE软件进行系统仿真,并利用拟合优度方法验证系统有效性。应用灵敏度分析方法,结合工作实际,得到高等院校协同创新管理启示如下：制定以科技型人才为主导的协同创新战略,强化高等院校协同创新的政策制度支撑,完善高等院校协同创新投入机制。     

新兴工业区人才聚集关键影响因素实证研究

将区域人才聚集关键影响因素归纳为组织—产业—区域三层次互动所形成的综合环境因素和人才服务—人才市场—人才制度三方构成的人才发展政策因素。对曹妃甸新兴工业区的实证研究表明,综合环境因素对现阶段该区域人才聚集影响显著,而人才发展政策因素的影响并不显著。     

Exact decomposition of the Fisher ideal total factor productivity index

Decompositions of total factor productivity (TFP) shed light on the driving factors behind productivity change. We develop the first exact decomposition of the Fisher ideal TFP index which contains no debatable mixed-period components or residuals. We systematically isolate five effects of (1) technical change, (2) technical efficiency, (3) scale efficiency, (4) allocative efficiency, and (5) price effect. The three efficiency components (2–4) represent the efficiency of achieving a given target point. Components (1) and (5) capture the changes of the target point. While the technical change component is well-established, changes in the relative input–output prices can have real effects on the scale and scope of the target. Such changes are captured by the new price effect component (5). The new decomposition is compared with existing decompositions both in theory and by means of an empirical application to a panel data of 459 Finnish farms in years 1992–2000.