城市家庭住房不平等:户籍、禀赋还是城市特征？——基于广义有序模型与Oaxaca-Blinder分解

基于中国劳动力动态调查（CLDS）数据，运用广义有序Logit模型实证分析户籍、家庭禀赋和城市特征三类因素对城市家庭住房不平等的影响；运用夏普利值分解、Oaxaca-Blinder分解区分"努力"和"环境"因素、户籍歧视和禀赋效应的贡献。研究发现：户籍可解释住房等级不平等的54.2%；"努力"因素对住房水平起正向作用；在户籍间住房等级不平等中户籍歧视效应占主导，在住房面积不平等中禀赋效应占主导，外地户口居民住房水平低源于"环境"因素趋弱，农业户口居民则源于"努力"和"环境"因素叠加趋弱；由于生活成本效应占主导，城市规模和流动人口占比越大，居民住房等级趋低，而城市土地供给增长有助于提升住房水平。     

CLOSED FORM PRICING FORMULAS FOR DISCRETELY SAMPLED GENERALIZED VARIANCE SWAPS

Most of the existing pricing models of variance derivative products assume continuous sampling of the realized variance processes, though actual contractual specifications compute the realized variance based on sampling at discrete times. We present a general analytic approach for pricing discretely sampled generalized variance swaps under the stochastic volatility models with simultaneous jumps in the asset price and variance processes. The resulting pricing formula of the gamma swap is in closed form while those of the corridor variance swaps and conditional variance swaps take the form of one‐dimensional Fourier integrals. We also verify through analytic calculations the convergence of the asymptotic limit of the pricing formulas of the discretely sampled generalized variance swaps under vanishing sampling interval to the analytic pricing formulas of the continuously sampled counterparts. The proposed methodology can be applied to any affine model and other higher moments swaps as well. We examine the exposure to convexity (volatility of variance) and skew (correlation between the equity returns and variance process) of these discretely sampled generalized variance swaps. We explore the impact on the fair strike prices of these exotic variance swaps with respect to different sets of parameter values, like varying sampling frequencies, jump intensity, and width of the monitoring corridor.     

快速城镇化地区不同生计类型农户耕地利用集约度评价 ——以上海市青浦区为例

研究目的:针对快速城镇化地区农户生计分化现象,以上海市青浦区为例,对不同生计策略类型农户的耕地利用集约度进行评价,并分析其影响因素,以期为改善农户土地利用行为、实现土地资源合理高效利用与农户生计可持续发展提供政策参考。研究方法:运用价值形态测度方法核算各生计类型农户的耕地利用集约度,以两水平方差成分模型确定耕地利用集约度影响因素。研究结果:(1)不同生计策略类型农户耕地利用集约度存在差异性,大小顺序依次为专业农业型、传统农业型、农业兼业型、非农兼业型,劳动集约度与资本集约度排序与之基本一致;(2)同一生计策略类型农户的耕地利用集约度存在相似性,耕地利用集约度的总变异中有24.32%是由农户生计策略类型不同而引起的,水平一中农业收入比重、农业补贴、耕地面积、地块平均离家距离、农户离集镇距离与水平二中是否兼业等是影响农户耕地利用集约度的显著因素;(3)模型拟合效果评价表明该方法具有可行性。研究结论:耕地利用集约度不仅与农户自身禀赋有关,而且受生计类型影响,政府相关部门应结合当地实际情况引导农户对这两方面进行调整或完善。     

Unrelated question model with two decks of cards

In this paper, we propose a new unrelated question model for estimating the prevalence of a sensitive characteristic within a population by utilizing two decks of cards. The resultant estimator is then compared to its competitors as to efficiency and as to protection of respondents. A real data application analyzing e-cigarette use among college students is considered.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

Informal Employment and the Structure of Wages in India: A Review of Trends

The “alternative”, “atypical” or “informal” workforce has grown in developed and developing countries alike. One of the more recent evolutions of informal employment has been of informal employment within formal enterprises. In the interest of flexibility and cost‐reduction, many formal firms increasingly resort to hiring workers on a temporary or informal basis. Alongside, and perhaps, as a result of the persistence and pervasiveness of informal employment, issues relating to inequality have come to the fore. This paper is motivated by these two intertwining aspects of Indian labor market—informality and wage inequality. Using nationally representative sample data, the paper examines trends in wage inequality among various forms of informal workers, overlaying these findings with broader trends in inequality. Using a regression based inequality decomposition, the paper compares the sources of wage inequality across different employment groups and the reasons for differences in wage inequality.     

Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting,incorporating realized measures

This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.     

基于web的高校教学与科研精细化管理模式研究

本文探讨了将高校教学与科研目标管理转换为多层次指标和权重的绩效管理的方法,采用精细化的管理理论,设计了利用网络和计算机技术解决高校教学与科研的考核与奖罚的管理模式。对高校信息化、精细化、标准化管理和过程控制具有重要意义。     

Earnings Instability and Tenure

We study the effect of tenure on earnings instability in Italy using the reforms of temporary employment contracts, which affected the average tenure of workers differentially across cohorts. We develop a model of earnings dynamics, and we exploit the variation of tenure and instability over time and across birth cohorts to estimate policy‐relevant parameters. Our results indicate that each year of tenure on the job reduces earnings instability by 11 percent; the drop is faster in the first three years of the match. Workers on a temporary contract have an earnings instability up to 100 percent higher than workers on a permanent contract.     

Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical analysis show that the model selects to use intermodal hubs located in areas with low disruption probabilities. In case of a disaster, the reliable solution results in 6.21% savings over the minimum cost solution.