Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems

This paper reviews a set of recent studies that have attempted to measure the causal effect of education on labor market earnings by using institutional features of the supply side of the education system as exogenous determinants of schooling outcomes. A simple theoretical model that highlights the role of comparative advantage in the optimal schooling decision is presented and used to motivate an extended discussion of econometric issues, including the properties of ordinary least squares and instrumental variables estimators. A review of studies that have used compulsory schooling laws, differences in the accessibility of schools, and similar features as instrumental variables for completed education, reveals that the resulting estimates of the return to schooling are typically as big or bigger than the corresponding ordinary least squares estimates. One interpretation of this finding is that marginal returns to education among the low‐education subgroups typically affected by supply‐side innovations tend to be relatively high, reflecting their high marginal costs of schooling, rather than low ability that limits their return to education.     

消费者异质偏好结构下动态创新竞合系统定价与创新策略研究

基于消费者对产品创新属性和价格属性的异质偏好结构，针对潜在创新企业和非创新企业间的动态竞合关系，构建了潜在创新企业和非创新企业间的集中式决策模型和Nash分散式决策模型，进而分析消费者异质偏好结构对潜在创新企业和非创新企业决策的影响机理。研究表明：(1)相较于分散式决策系统，集中式决策系统是把双刃剑。集中式决策系统虽然提升了非创新企业和潜在创新企业的产品定价、系统整体利润，抑制了非创新企业发展，压缩了落后产能；但是，集中式决策亦抑制了潜在创新企业的产品创新活动，降低了创新产品产能，不利于社会的创新研发。(2)随着消费者对产品创新属性偏好的增加，潜在创新企业产品定价和供货量上升，非创新企业产品定价和供货量下降；系统利润先下降后上升，非创新企业利润一致单调下降，潜在创新企业利润一致单调上升。(3)随着产品创新难度的增加，潜在创新企业以及非创新企业的产品定价、系统盈利水平均下降。此外还发现，产品相对创新度对系统主体决策存在复杂影响。     

互联网使用与家庭间接碳排放:测度及影响因素分析

面临全球气候变暖和能源短缺的双重危机，我国在明确提出“双碳”目标后，党的二十大报告再次强调了绿色低碳产业和绿色低碳生活方式在实现高质量发展中的关键作用。在此背景下，深入探讨家庭这一重要能耗部门的能源消费及其碳排放状况，对于探寻家庭碳减排的有效路径具有重要意义。本文以占据家庭主要能源消耗的间接用能为代表，以中国家庭追踪调查(CFPS)数据为基础，基于消费生活方式法测度了家庭间接碳排放量，并通过构建双向固定效应模型实证分析了互联网使用对家庭碳排放的影响；在此基础上，采用中介效应模型和Bootstrap法共同验证了互联网使用影响家庭间接碳排放的作用路径；并进一步基于家庭所在地区、城乡类别以及收入水平差异这三个视角，探讨了互联网使用对家庭间接碳排放的异质性影响。研究发现：首先，互联网使用正向促进了家庭间接碳排放，对享受型消费碳排放和发展型消费碳排放的增加效应最为显著，而对生存型消费碳排放则没有明显的影响。其次，互联网使用主要通过网络购物这一中介变量对家庭间接碳排放产生正向影响。最后，异质性分析表明：互联网使用对东部、中部以及西部地区家庭的间接碳排放均有显著的正向影响，且作用强度从中部、西部到东...     

Estimation When a Parameter is on a Boundary

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: (i) quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and (ii) LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.     

Variable selection in heterogeneous panel data models with cross-sectional dependence

This paper studies the Bridge estimator for a high-dimensional panel data model with heterogeneous varying coefficients, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We establish oracle efficiency and the asymptotic distribution of the Bridge estimator, when the number of covariates increases to infinity with the sample size in both dimensions. A BIC-type criterion is also provided for tuning parameter selection. We further generalise the marginal Bridge estimator for our model to asymptotically correctly identify the covariates with zero coefficients even when the number of covariates is greater than the sample size under a partial orthogonality condition. The finite sample performance of the proposed estimator is demonstrated by simulated data examples, and an empirical application with the US stock dataset is also provided.     

Regression analysis of multivariate current status data under a varying coefficients additive hazards frailty model

This article discusses regression analysis of multivariate current status failure time data for which the observation time may be related to the underlying survival time. A local partial likelihood technique is used to estimate the varying coefficient covariate effect functions under the additive hazards frailty model. The asymptotic properties of the proposed estimators are established. An extensive simulation study is conducted for the evaluation of the proposed procedure, the results of which indicate that the proposed method works well in practice. Also, a real data study is provided to illustrate the performance of the proposed method.