中国经济增长模式转型：供给结构视角分析

本文从供给结构角度分析了自1978年以来中国经济增长模式转型的特征，并针对存在的问题提出了相应的解决对策，旨在寻求经济增长模式转型的有效路径。     

货币经济与总量关系--马克思经济学和凯恩斯经济学的异曲同工

与居于现代主流经济学地位、以强调技术关系分析为核心的新古典理论不同,马克思经济学和凯恩斯经济学基于市场经济或资本主义经济中社会关系竞争的博弈规则,认为资本主义生产目的即在于获取按货币价值计算的利润或货币增殖;通过对货币经济背景下资本主义经济关系的剖析,马克思和凯恩斯分别建立了自己的宏观经济理论体系。总量分析方法的采用,不仅使凯恩斯开创了现代经济学分析范式的新的体系,也使得对新古典“两分法”的批判能够联系到现实GDP核算体系的计量,从而使人们重新认识到马克思宏观经济学对现实资本主义经济体系的重要意义。马克思经济学与凯恩斯经济学对资本主义总量货币经济性质的理解有着共通之处,这正是其经济学革命之共同意义所在。     

中国技能偏态性技术变迁的实证检验:兼论大学教育溢价

本文利用中国2002-2010年地区面板数据,实施空间面板估计技术实证检验了我国技能偏态性技术进步的存在性。研究表明：我国现阶段技术呈现技能偏态特征,技术水平呈现东-中-西梯度发展格局;地区间的经济发展、要素投入以及人力资本水平呈现发散效应;相对于以往研究,本文规模报酬可变的总量生产函数设计可以更合理地逼近中国经济增长过程,空间相关性的诊断使我们能更好地评价中国不同地区、不同发展阶段是否存在技术异质性特征;最后考虑到现阶段大学生平均就业难的事实,提出应充分考虑到劳动力市场的多元分割特征,实现技能偏态技术与大学生供给有效对接的政策建议。     

Overcoming biases and misconceptions in ecological studies

The aggregate data study design provides an alternative group level analysis to ecological studies in the estimation of individual level health risks. An aggregate model is derived by aggregating a plausible individual level relative rate model within groups, such that population-based disease rates are modelled as functions of individual level covariate data. We apply an aggregate data method to a series of fictitious examples from a review paper by Greenland and Robins which illustrated the problems that can arise when using the results of ecological studies to make inference about individual health risks. We use simulated data based on their examples to demonstrate that the aggregate data approach can address many of the sources of bias that are inherent in typical ecological analyses, even though the limited between-region covariate variation in these examples reduces the efficiency of the aggregate study. The aggregate method has the potential to estimate exposure effects of interest in the presence of non-linearity, confounding at individual and group levels, effect modification, classical measurement error in the exposure and non-differential misclassification in the confounder.     

Uses and limitations of dichotomous aggregate relational data

Aggregate relational data (ARD) on relationships between individuals and subgroups have been informative for studying egocentric network size and degree distributions, assessing segregation in contact with subpopulations, and estimating the size of unlisted groups. Scale-up models for ARD usually assume survey data giving counts of acquaintances in subpopulations, but a closed-ended response format that asks respondents to select a category covering a range of counts may be less burdensome. The simplest (dichotomous) such format distinguishes between having one or more acquaintances in a subpopulation and having none; many existing position generator data take this form. We assess the potential of dichotomous ARD by adapting existing methods for inference from count ARD to accommodate such data. We find that they permit estimation of degree distributions for basic scale-up models, though estimates are less precise than those from count ARD. Dichotomous ARD do not contain sufficient information to model segregation, however. These limitations may be addressed by estimating segregation using respondent heterogeneity on observed covariates, and/or by using a slightly expanded closed response format (0, 1, or two or more acquaintances). Our results suggest how the applications of position generator data – which typically collect dichotomous ARD – might be broadened to encompass questions studied using ARD.     

Assessment of Seismic Loss Dependence Using Copula

The catastrophic nature of seismic risk is attributed to spatiotemporal correlation of seismic losses of buildings and infrastructure. For seismic risk management, such correlated seismic effects must be adequately taken into account, since they affect the probability distribution of aggregate seismic losses of spatially distributed structures significantly, and its upper tail behavior can be of particular importance. To investigate seismic loss dependence for two closely located portfolios of buildings, simulated seismic loss samples, which are obtained from a seismic risk model of spatially distributed buildings by taking spatiotemporally correlated ground motions into account, are employed. The characterization considers a loss frequency model that incorporates one dependent random component acting as a common shock to all buildings, and a copula‐based loss severity model, which facilitates the separate construction of marginal loss distribution functions and nonlinear copula function with upper tail dependence. The proposed method is applied to groups of wood‐frame buildings located in southwestern British Columbia. Analysis results indicate that the dependence structure of aggregate seismic losses can be adequately modeled by the right heavy tail copula or Gumbel copula, and that for the considered example, overall accuracy of the proposed method is satisfactory at probability levels of practical interest (at most 10% estimation error of fractiles of aggregate seismic loss). The developed statistical seismic loss model may be adopted in dynamic financial analysis for achieving faster evaluation with reasonable accuracy.     

Understanding the Risk-Return Relation: The Aggregate Wealth Proxy Actually Matters

ABSTRACT

The ICAPM implies that the market’s conditional expected return is proportional to its conditional variance and that the reward-to-risk ratio equals the representative investor’s coefficient of relative risk aversion. Prior studies examine this relation using the stock market to proxy for aggregate wealth and find mixed results. We show, however, that stock-based tests suffer from low power and lead to biased estimates of the risk-return tradeoff when stocks are an imperfect market proxy. Tests designed to mitigate this bias by incorporating a more comprehensive measure of aggregate wealth produce large, positive estimates of the risk-aversion coefficient around seven to nine. Supplementary materials for this article are available online.     

Development of Specific hypothesis tests for estimated markov chains

We develop and evaluate the validity and power of two specific tests for the transition probabilities in a Markov chain estimated from aggregate frequency data. The two null hypotheses considered are (1) constancy of the diagonal elements of the one-step transition probability matrix and (2) an arbitrarily chosen transition probability’s being equal to a specific value. The formation of tests uses a general framework for statistical inference on estimated Markov processes; we also indicate how this framework can be used to form tests for a variety of other hypotheses. The validity and power performance of the two tests formed in this paper are examined in factorially designed Monte Carlo experiments. The results indicate that the proposed tests lead to type I error probabilities which are close to the desired levels and to high power against even small deviations from the null hypotheses considered.     

An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming

In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.     

Some Limitations of Aggregate Exposure Metrics

Aggregate exposure metrics based on sums or weighted averages of component exposures are widely used in risk assessments of complex mixtures, such as asbestos-associated dusts and fibers. Allowed exposure levels based on total particle or fiber counts and estimated ambient concentrations of such mixtures may be used to make costly risk-management decisions intended to protect human health and to remediate hazardous environments. We show that, in general, aggregate exposure information alone may be inherently unable to guide rational risk-management decisions when the components of the mixture differ significantly in potency and when the percentage compositions of the mixture exposures differ significantly across locations. Under these conditions, which are not uncommon in practice, aggregate exposure metrics may be "worse than useless," in that risk-management decisions based on them are less effective than decisions that ignore the aggregate exposure information and select risk-management actions at random. The potential practical significance of these results is illustrated by a case study of 27 exposure scenarios in El Dorado Hills, California, where applying an aggregate unit risk factor (from EPA's IRIS database) to aggregate exposure metrics produces average risk estimates about 25 times greater - and of uncertain predictive validity - compared to risk estimates based on specific components of the mixture that have been hypothesized to pose risks of human lung cancer and mesothelioma.