The identification of fiscal and monetary policy in a structural VAR

Good economic management depends on understanding shocks from monetary policy, fiscal policy and other sources affecting the economy and their subsequent interactions. This paper presents a new methodology to disentangle such shocks in a structural VAR framework. The method combines identification via sign restrictions, cointegration and traditional exclusion restrictions within a system which explicitly models stationary and non-stationary variables and accounts for both permanent and temporary shocks. The usefulness of the approach is demonstrated on a small open economy where policy makers are actively considering the interaction between monetary and fiscal policies.     

Avoiding arbitrary exclusion restrictions using ratios of reduced-form estimates

We show how to obtain coherent structural-form (SF) exclusion restrictions using the reduced-form (RF) parameter ratios. It will be shown that an over-identified SF corresponds to a group of regressors sharing the same RF ratio value; those regressors should be excluded jointly from the SF. If there is no group structure, then the SF is just-identified; in this case, however, it is no longer clear which regressor should be excluded. Hence, just-identified SF’s are more arbitrary than over-identified SF’s in terms of exclusion restrictions. This is in stark contrast to the notion that the former is less arbitrary than the latter, because the former excludes fewer regressors. We formalize these points, and then suggest to find the number of modes in the estimated RF ratios as a way to find groups in the ratios. For this purpose, an informal graphical method using a kernel nonparametric method and a formal modality test are employed. An empirical example with selling price in a residential real estate market and duration on the market as two endogenous variables is provided. The authors are grateful to the editor and two anonymous referees for their comments     

Entrepreneurship through a qualitative lens: Insights on the construction and/or discovery of entrepreneurial opportunity

This article applies inductive analytic techniques to identify and elaborate on two recurring themes that underpin the core puzzle of entrepreneurship research — where entrepreneurial opportunities come from. The first theme is the unique role of imprinting, or the profound influence of social and historical context in constraining the perceptual apparatus of entrepreneurs and delimiting the range of opportunities for innovation available to them. Second, our analysis offers insight into the counterbalancing role of reflexivity, operating at both individual and collective levels of analysis, in generating the ability of entrepreneurs to overcome the constraints of imprinting. These insights are based on a thematic review of the nine studies that comprise this special issue on qualitative research. The nine studies, individually and each in their own way, offer key insights into how we might better understand the emergence of entrepreneurial opportunity.     

柴胡口服液的工艺研究

目的探讨完善柴胡口服液中的柴胡提取工艺及其参数的方法。方法以药典质量标准为指标,通过调整浸渍方法、浓缩温度和时间、冷藏条件和p H值、辅料用量等方法,解决原工艺产品鉴别和澄清度不合格的质量问题。结果通过增加药材冷浸12 h提高了挥发油的提取率;通过控制浓缩温度和时间尽可能减少了柴胡皂苷d的分解;通过正交试验筛检,将得到的提取液置于1℃、p H值为7.0条件下冷藏48 h,将杂质有效除去。通过增大丙二醇用量增加了产品的溶解稳定性。结论按改进后的工艺条件生产,三批成品质量合格,初步稳定性试验结果表明该工艺稳定、可行。     

出口机会与进口竞争对工业机器人企业的叠加影响：基于出口持续时间的视角

工业机器人企业是中国推动制造业数字化革命、抢占国际分工制高点战略的主力军,然而其出口持续时间平均不足1年,需引起高度关注。本文基于2000-2015年中国海关数据库和工业企业数据库的匹配数据,运用Cox风险模型分析了出口机会增大和进口竞争加剧对工业机器人企业出口持续时间的影响和作用机制。结果表明,出口机会增大更有利于延长企业出口持续时间,进口竞争加剧则提高了企业的退出率,这一结论在产品多样化程度低、产品核心度低、行业竞争小和市场集中度低的企业中尤为明显;出口机会与进口竞争,主要是通过企业生产率和技术创新的中介作用影响企业的出口持续时间;面对双重叠加影响,工业机器人企业应充分利用产品间的技术关联效应提高出口持续性。     

Towards a schematic theory of entrepreneurial alertness

This article is an investigation into Kirzner's concept of entrepreneurial alertness — its mechanism and its antecedents. By drawing from decision theory and schema theory, a model is developed to show how changes in the environment are mediated by entrepreneurial alertness and brought to the situated attention of entrepreneurs for evaluation. Entrepreneurial alertness is seen to be the application of unique schemata that allow the entrepreneur to impute meaning to environmental change that would not be imputed by other managers. It is argued that the alertness that allows entrepreneurs to see opportunity where others do not arises from differences in schematic richness, schematic association, and schematic priming. These three antecedents may therefore form a basis on which enhanced entrepreneurial alertness can be developed.     

Semiparametric diffusion estimation and application to a stock market index

The analysis of diffusion processes in financial models is crucially dependent on the form of the drift and diffusion coefficient functions. A new model for a stock market index process is proposed in which the index is decomposed into an average growth process and an ergodic diffusion. The ergodic diffusion part of the model is not directly observable. A methodology is developed for estimating and testing the coefficient functions of this unobserved diffusion process. The estimation is based on the observations of the index process and uses semiparametric and non-parametric techniques. The testing is performed via the wild bootstrap resampling technique. The method is illustrated on S&P 500 index data.     

Environmental cost and eco-efficiency from vessel emissions in Las Palmas Port

This study presents external costs and eco-efficiency parameters associated to exhaust emissions in Las Palmas Port. Emission assessment is based on a vessel emissions inventory obtained from the full bottom-up Ship Traffic Emission Assessment Model and messages transmitted by the Automatic Identification System over 2011. External costs are estimated based on a top-down approach. Results are combined with port operations profiles resulting in eco-efficiency performance towards economic and environmental concerns in Las Palmas Port. Results could also support the valuation of instruments to abate emissions in crowded port-cities that as Las Palmas, host a large population of residents and visitors.     

Identification and estimation with contaminated data: When do covariate data sharpen inference?

Contaminated or corrupted data typically require strong assumptions to identify parameters of interest. However, weaker assumptions often identify bounds on these parameters. This paper addresses when covariate data—variables in addition to the one of interest—tighten these bounds. First, we construct the identification region for the distribution of the variable of interest. This region demonstrates that covariate data are useless without knowledge about the distribution of erroneous data conditional on the covariates. Then, we develop bounds both on probabilities and on parameters of this distribution that respect stochastic dominance.     

Excess heterogeneity, endogeneity and index restrictions

A discrete or continuous outcome is determined by a structural function in which the effect of some variables of interest is transmitted through a scalar index. Multiple sources of stochastic variation can appear as arguments of the structural function, but not in the index. There may be endogeneity, that is observable and unobservable variables may not be independently distributed. Conditions are provided under which there is local identification of measures of the relative sensitivity of the index to variations in pairs of its possibly endogenous arguments, namely ratios of partial derivatives of the index.