大数据背景下区域木材物流配送中心选址

指出了我国木材物流存在的主要问题以及解决方案,结合大数据技术,利用遗传算法进行木材物流配送中心的选址,建立了数学模型并进行了案例分析,以达到对木材物流成本和服务的优化。     

“Opt out” or kept out? The effect of stigma,structure, selection,and sector on the labor force participation of married women in India

Using the 61st and 68th rounds of National Sample Survey data, we investigate the role of stigma, the economy’s structure, potential selection bias, and sectoral differences in explaining the low labor force participation (LFP) of middle and secondary educated women in India relative to low‐ and high‐educated women (the “U‐shape”). Estimating LFP regressions on a sample of prime‐aged married women, we show that, controlling for background characteristics, if a woman’s husband works in a white‐collar job in a region with a high share of blue‐collar jobs, she is less likely to participate in the labor market (stigma). We also find a positive effect of an increase in an index of white‐collar job growth (structure) on married women’s LFP. These effects are present in both rural and urban sectors but are strongest in the rural sector. However, middle and secondary educated women are still found to have substantially lower LFP than low‐ and graduate‐educated women in both sectors. Indeed, over time, the U‐shape persists in the rural sector and deepens in the urban sector. Because unobservables are quite large in the urban sector, we use the method of instrumental variables and find an increasing return at middle levels but stagnation at higher levels.     

Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models

Abstract

Foreign exchange markets affect a variety of humans and businesses worldwide and there is a wide array of literature aimed at providing more accurate forecasts of their movement. In an attempt to quantify human expectations, Google query search terms related to foreign exchange markets are used to help explain and predict foreign exchange rates between the United States’ dollar and ten other currencies during the time period of January 2004 and August 2018. We find evidence that, while Google Trends can be helpful in prediction, it is necessary to implement some sort of shrinkage or sparsity scheme on the coefficients.     

制度距离、投资动机与企业OFDI区位选择——中国对外直接投资“制度风险偏好”的一种解释

由于企业的对外直接投资（OFDI）存在制度适应成本，东道国与母国之间的制度距离会对企业OFDI产生负向影响。基于2003—2015年中国对140个国家和地区OFDI的分析表明：中国企业的OFDI具有明显的市场寻求动机和战略资源寻求动机，其中，市场寻求型OFDI表现出显著的互补效应，战略资源寻求型OFDI表现出一定程度的挤出效应；东道国与中国的制度距离会通过投资动机影响中国企业OFDI的区位选择，即倾向于向制度距离较小的国家和地区进行OFDI，且制度距离会弱化市场寻求型OFDI的互补效应和战略资源寻求型OFDI的挤出效应。站在企业角度来看，中国企业在进行OFDI时并不具有“制度风险偏好”，而是表现出“制度风险规避”特征：选择对其自身发展来讲制度风险较小（制度距离较小）的目标市场进行OFDI。因此，中国应深化市场经济体制改革，提升国内制度质量，加强国际经济合作，并增强企业的制度适应能力，以有效减小制度距离对企业OFDI区位选择的约束，进而优化OFDI区位分布，提高OFDI效益和质量。     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

Integrated planning for product selection,shelf-space allocation,and replenishment decision with elasticity and positioning effects

As the retail industry is growing larger and more diversified, retailers' decisions about product selection, shelf-space-allocation, and replenishment become more important and challenging. This paper is to present a model for shelf-space allocation with product selection and replenishment decisions to maximize the retailer's profit. The model is based on a two-dimensional display space in which all shelves and products have widths and heights and includes factors that influence demand for each product, such as space and cross-space elasticities and positioning effects. The integrated model presented is mixed-integer non-linear programming (MINLP) because the demand function is non-convex. This research proposes two heuristic algorithms (tabu search and genetic) to solve the MINLP problem. The results show the effectiveness and efficiency of these algorithms by comparing the outputs to the MINLP optimal solution for small data sets and comparing the algorithm performances for large data sets. The solution methodologies expect to support a simultaneous decision-making process for retailers to maximize their revenue.     

Assessing effects of federal crop insurance supply on acreage and yield of specialty crops

Crop insurance may affect harvested acreage and yield by influencing producers’ behavior such as land allocation and input use. Although specialty crops are a major source of farm income, especially on the U.S. west coast, they have not received as much attention as field crops in previous empirical studies. This paper assesses the effect of moral hazard and adverse selection associated with the federal crop insurance program (FCIP) on the acreage and yield of major specialty crops in California. An econometric method that expands the switching regression model is developed to assess the effect. Results suggest that federal crop insurance can change specialty crop growers’ production responses to climate and soil conditions. The moral hazard effect tends to increase the acreage and yield of the specialty crops, whereas the adverse selection effect tends to have the opposite effect. The overall effect of the FCIP on acreage and yield of specialty crops is found to be moderate.     

International stock return predictability

We propose a new methodology for predicting international stock returns. Our Bayesian framework performs probabilistic selection of predictors that can shift at multiple unknown structural break dates. The approach generates significantly more accurate forecasts of international stock returns than a range of popular models that are economically meaningful for a risk-averse mean–variance investor. Allowing for regime-specific variable selection reduces considerably the international diversification of an unhedged U.S. investor’s portfolio.     

利用双正交小波变换优化G3-PLC系统性能

为了提高G3-PLC(G3-Power Line Communication)在复杂信道环境下通信的可靠性，提出了基于双正交小波变换的正交频分复用（Discrete Wavelet Transform-Orthogonal Frequency Division Multiplexing,DWT-OFDM）。通过双正交小波变换减小复杂信道环境下G3-PLC通信系统中OFDM载波间干扰，并通过合理减少循环前缀，优化传输效率，降低峰均比。在DWT-OFDM的G3-PLC系统中，通过不同小波基性能分析与对比，利用bior6.8双正交小波基进行系统性能优化。将DWT-OFDM的G3-PLC系统在实际电力线噪声库环境下进行实验分析，仿真结果表明，在平均误码率为10-3量级时，性能有5 dB左右提升，通信效率由40%提高至54%；结合限幅法，峰均比由11 dB降低至8 dB，通信性能极大优于基于FFT-OFDM的G3-PLC系统。     

一种利用机器学习优化MIMO窃听信道天线选择的方法

为保障多输入多输出窃听信道系统中信息传输的保密性，提出了一种基于机器学习的天线选择方案。首先利用机器学习解决分类问题准确率高、处理大数据高效这一优势，设计了基于奇异值分解的特征值提取、基于信干噪比的标签赋值方案，建立了k最近邻分类器和逻辑回归分类器选择最优天线最大化保密性能（可达保密速率和保密中断概率）。与传统天线选择方案相比，所提方案获得了几乎一致的保密性能，并且大幅降低了系统的选择复杂度和误比特率。