融资约束与零售企业经营模式选择

回归零售本质、强化自营能力是零售企业实现可持续发展的关键路径,然而当前众多零售企业放弃了自营,更偏好联营。为找出这一矛盾的症结所在,基于不完全契约理论,构建了融资约束背景下不同零售企业经营模式对应的供应商和零售商之间的收益分配模型,将自营率作为零售经营模式变量引入理论模型,以解释零售业联营模式占优的现象。理论分析结果显示,融资约束的提升会降低零售企业的自营率。进一步地,利用2017—2019年我国境内经营的176家零售企业相关数据进行实证分析发现,融资约束在很大程度上限制了我国零售企业对自营模式的自由选择,且私营零售企业和百货零售企业受融资约束的影响更大。因此,一方面要拓宽融资渠道、优化资本结构、加强市场监管,为零售企业提供良好的融资条件;另一方面要通过品牌建设、供应链完善、提高市场集中度等,强化零售企业的自营能力。     

社会保险成本过高是否抑制企业投资?

社会保险成本过高是否抑制企业投资，从而阻碍实体经济发展?对此，文章利用工业企业微观数据实证检验了企业社保成本变动对投资率的影响.为缓解内生性问题，文章选择城市老年人比重作为工具变量.实证检验显示企业社保支出比重增加1个单位标准差，企业投资率约下降3.3%，能够解释企业投资率变动的6.4%，具有较强经济显著性.文章还探究了社保成本变动影响企业投资的内在机制，发现劳动成本渠道、生产率渠道以及融资约束渠道都得到相关经验证据的支持.研究结果说明降低企业社保成本的政策如果能够得到有效落实，将会刺激企业扩大投资，促进地区经济发展.     

Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework

Abstract

A key question for understanding the cross-section of expected returns of equities is the following: which factors, from a given collection of factors, are risk factors, equivalently, which factors are in the stochastic discount factor (SDF)? Though the SDF is unobserved, assumptions about which factors (from the available set of factors) are in the SDF restricts the joint distribution of factors in specific ways, as a consequence of the economic theory of asset pricing. A different starting collection of factors that go into the SDF leads to a different set of restrictions on the joint distribution of factors. The conditional distribution of equity returns has the same restricted form, regardless of what is assumed about the factors in the SDF, as long as the factors are traded, and hence the distribution of asset returns is irrelevant for isolating the risk-factors. The restricted factors models are distinct (nonnested) and do not arise by omitting or including a variable from a full model, thus precluding analysis by standard statistical variable selection methods, such as those based on the lasso and its variants. Instead, we develop what we call a Bayesian model scan strategy in which each factor is allowed to enter or not enter the SDF and the resulting restricted models (of which there are 114,674 in our empirical study) are simultaneously confronted with the data. We use a Student-t distribution for the factors, and model-specific independent Student-t distribution for the location parameters, a training sample to fix prior locations, and a creative way to arrive at the joint distribution of several other model-specific parameters from a single prior distribution. This allows our method to be essentially a scaleable and tuned-black-box method that can be applied across our large model space with little to no user-intervention. The model marginal likelihoods, and implied posterior model probabilities, are compared with the prior probability of 1/114,674 of each model to find the best-supported model, and thus the factors most likely to be in the SDF. We provide detailed simulation evidence about the high finite-sample accuracy of the method. Our empirical study with 13 leading factors reveals that the highest marginal likelihood model is a Student-t distributed factor model with 5 degrees of freedom and 8 risk factors.     

政府行为、金融环境与区域创业差距——基于中国省际面板数据的实证分析

中国作为一个正处于转型过程中的发展中国家，创业活动类型和创业环境都与成熟市场经济国家存在很大差异，经济体系中呈现"强政府、弱市场"的资源配置格局，金融市场化尚不完善。研究中国的区域创业差距问题必须基于中国目前转型经济条件下的特殊背景。文章旨在解决以下两个问题：政府行为和金融约束对中国区域创业差距产生了怎样的影响?中国转型背景下政府行为和金融市场的特殊性是否会对中国区域创业产生不同于成熟市场经济的影响?文章在研究框架上对流行的创业折衷框架进行修正，创新性地引入政府的行政规范程度、非税负担变量和宏观流动性约束变量，建立省际面板数据模型，实证分析了政府行为和金融约束对中国区域创业差距的影响。研究结果表明：政府管制程度的提高，无论对生存型创业还是机会型创业都将产生抑制作用；信贷环境方面，在目前金融市场化尚不完善的背景下，银行信贷资金在中国整个企业融资体系中仍然占据主导地位，信贷约束的放松和信贷可获得性的增强对中国的机会型创业行为产生显著的促进作用。     

On the structuralist constraints in social scientific theorizing

Several case studies and theoretical reports indicate that the structuralist concept of a constraint has a central role in the reconstruction of physical theories. It is surprising that there is only little theoretical discussion on the relevance of constraints for the reconstruction of social scientific theories in the literature. Almost all structuralist reconstructions of social theorizing are vacuously constrained. Consequently, constraints are methodologically irrelevant.In this paper I reconstruct three cases selected from social scientific theorizing. The first case is a generalization of Gross, Mason and McEachern's (1958) role expectation conflict theory, which is a qualitative theory. The second case is Cohen and Lee's (1975) quantitative theory of social conformity, which essentially utilizes the theor of Markov chains. The third case is the explanation schema of folk psychology which is one of the most important methodological frameworks in the social sciences.In all the three cases important constraints emerge. From an epistemological point of view it seems that the related constraints work as higher-order laws. In any case, purely conceptual arguments are insufficient to justify them.The three cases,mutatis mutandis, cover nearly all types of social scientific theorizing, so that in my view constraints play a crucial methodological role in the social sciences precisely as they have in the natural sciences.     

非线性极大极小问题的可行信赖域算法

针对一类非线性约束极大极小问题,利用极大熵方法将转化为带不等式约束的非线性规划问题,给出了一种可行信赖域算法,解决了不等式约束的非线性大系统优化问题,并证明了该算法的全局收敛性。初步的数值试验表明,对于该类极大极小问题,本算法有良好的数值表现。     

Some recent developments for regression analysis of multivariate failure time data

Cox's seminal 1972 paper on regression methods for possibly censored failure time data popularized the use of time to an event as a primary response in prospective studies. But one key assumption of this and other regression methods is that observations are independent of one another. In many problems, failure times are clustered into small groups where outcomes within a group are correlated. Examples include failure times for two eyes from one person or for members of the same family.This paper presents a survey of models for multivariate failure time data. Two distinct classes of models are considered: frailty and marginal models. In a frailty model, the correlation is assumed to derive from latent variables (frailties) common to observations from the same cluster. Regression models are formulated for the conditional failure time distribution given the frailties. Alternatively, marginal models describe the marginal failure time distribution of each response while separately modelling the association among responses from the same cluster.We focus on recent extensions of the proportional hazards model for multivariate failure time data. Model formulation, parameter interpretation and estimation procedures are considered.     

A procedure to select a ML-II prior in a multivariate normal case

Berger (1985) derived a procedure to select a maximum likelihood II prior distribution. In this paper a method is suggested to construct such a prior distribution from a multivariate ε-contamination class of distributions. The method is illustrated by the conetruction of a ML-II prior in the multivariate normal case.     

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.     

An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations

This paper addresses the resource-constrained project scheduling problem with uncertain activity durations. An adaptive robust optimization model is proposed to derive the resource allocation decisions that minimize the worst-case makespan, under general polyhedral uncertainty sets. The properties of the model are analyzed, assuming that the activity durations are subject to interval uncertainty where the level of robustness is controlled by a protection factor related to the risk aversion of the decision maker. A general decomposition approach is proposed to solve the robust counterpart of the resource-constrained project scheduling problem, further tailored to address the uncertainty set with the protection factor. An extensive computational study is presented on benchmark instances adapted from the PSPLIB.