工资对个人劳动供给的非线性影响：部门市场化程度差异视角

从市场经济体制的建立到经济社会高质量发展，我国不同部门市场化程度有所差异，劳动供给的部门异质性随之体现。基于中国劳动力动态调查数据，结合汉森门槛回归模型和劳动供给模型，依托部门市场化程度的差异性，文章探讨了工资对劳动力个人供给的非线性影响及其部门异质性。主要得出以下研究结论：第一，我国市场部门与公共部门劳动力的个人劳动供给有所差异。第二，工资与个人劳动供给呈现非线性关系并且存在部门异质性。实证分析结果显示，工资对个人劳动供给的影响存在三个"突变点"，工资门槛均使得工资"激励效应"加强并呈现逐步增加的趋势。第三，劳动供给的工资门槛存在性别、城乡和行业异质性。第四，随着年龄增长、人力资本积累，个人劳动时间供给趋于降低，签署书面劳工合同使得市场部门劳动力工作时间显著增加，而政治面貌显著影响公共部门劳动供给。总体来看，无论是市场部门还是公共部门，我国劳动力个人供给曲线均未"向后弯曲"，国民经济高质量发展、改善收入分配格局、提升居民幸福感等议题需持续关注。     

液晶显示器的应用

本文以２种具有代表性的液晶显示器为例，介绍了液晶显示器的硬件设计方法和软件编程方法。同时介绍了一种液晶显示器的汉字显示方法。     

总收益最大的交易策略求解算法

本文应用动态规划的原理,讨论了多期投资决策中基于总收益率最大的交易策略的设计和实现问题。通过比较随机交易方法、局部最优方法和全局最优方法下的投资表现,在交易费用存在的情况下,基于动态规划的算法给出问题的全局最优解,该方法的优越性随着交易费用的增长而加强。     

Lp空间上线性回归方程回归系数的估计

本文讨论了多元线性回归方程参数的L^p估计问题。首先采取消除方程中参数“。的技术，化参数估计为对回归系数的估计，然后进一步将问题转化为一个单目标数学规划模型，利用能自动搜索最优解的电脑软件，十分方便地求出规划模型的最优解。特别对一元回归方程，在消除参数a0后，还可以直接利用普通一元函数求极值的方法，求出回归系数的估计。     

A comparison of programming languages in macroeconomics

We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++14, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language.     

Symbolic regression for better specification

This note introduces the concept of symbolic regression (SR) to tourism and hospitality research. SR uses genetic programming to find the model that best fits the data without a need to pre-specify a functional form or to impose a certain model as a starting point. In other words, SR helps to uncover the intrinsic characteristics of the data at hand. Our view is that SR can serve as an improved method of testing for misspecification. In this note, we propose to derive the true functional form of the residual using SR. We then use this information to improve the forecasts of the linear regression model and, to perform hypothesis tests if needed.     

Voluntary disclosure,greenhouse gas emissions and business performance: Assessing the first decade of reporting

This study explores the empirical relationships between GHG emissions and an extensive range of business performance measures for UK FTSE-350 listed firms over the first decade or so of such reporting. Despite the popular and policy generated environmental imperatives over this period—along with growing evidence of the corporate added-value of having an ‘environmental conscience’, voluntary disclosure of emissions has been slow to adopt by firms. The leading contribution is to present clear evidence of a non-linear relationship, initially increasing with firm performance and then decreasing. An extensive pattern of non-reporting of emissions is also observed over time, and prior literature has introduced questions of endogeneity existing between firm performance and emissions. Steps are taken to ensure confidence/robustness of the results to these concerns. Accordingly, a two-stage (Heckman-type) selection model is used to analyse the emissions-performance nexus conditional upon the firm choosing to report (i.e. treating the choice to report as being endogenously determined with firm performance). From this—in addition to confirming the robustness of the non-linear relationship—it can be observed that the decision to report emissions is not directly influenced by wider social/governance disclosure attitudes of a firm, thus suggesting that firms disassociate environmental responsibility from social responsibility.     

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.     

Personalized goal-based investing via multi-stage stochastic goal programming

In this paper, we propose a goal-based investment model that is suitable for personalized wealth management. The model only requires a few intuitive inputs such as size of wealth, investment amount, and consumption goals from individual investors. In particular, a priority level can be assigned to each consumption goal and the model provides a holistic solution based on a sequential approach starting with the highest priority. This allows strict prioritization by maximizing the probability of achieving higher priority goals that are not affected by goals with lower priorities. Furthermore, the proposed model is formulated as a linear program that efficiently finds the optimal financial plan. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investment model provides a new framework for automated investment management services.