实现2035年发展目标的潜在增长率*

党的十九届五中全会提出了到2035年人均GDP达到中等发达国家水平的远景目标，因此测算和回答能否和如何如期实现该目标，对于我国实现第二个百年奋斗目标和坚持“四个自信”具有重要的意义。为此，本文根据跨越和陷入“中等收入陷阱”经济体的发展经验，对2021—2035年我国潜在增长率变化进行了测算。一是参照跨越和陷入“中等收入陷阱”经济体在我国相同发展阶段时各主要生产要素的变化，模拟设定我国未来各主要生产要素的增长率；二是通过运用附加人力资本的增长核算模型测算基准、乐观和悲观三种不同情境下未来我国经济的潜在增长率，验证我国2035年发展目标实现的可能性；三是依据主要要素对潜在增长率的贡献度，提出我国如期实现2035年发展目标的相应政策建议。     

Sentiment-scaled CAPM and market mispricing

This study explores the conditional version of the capital asset pricing model on sentiment to provide a behavioural intuition behind the value premium and market mispricing. We find betas (β) and the market risk premium to vary over time across different sentiment indices and portfolios. More importantly, the state β derived from this sentiment-scaled model provides a behavioural explanation of the value premium and a set of anomalies driven by mispricing. Different from the static β–return relation that gives a flat security market line, we document upward security market lines when plotting portfolio returns against their state βs and portfolios with higher state βs earn higher returns.     

Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

The moderating effect of average wage and number of stores on private label market share: A hierarchical linear model analysis

Existing research on private label market share is primarily in the context of the Western market. The Chinese market context research is scarce, although private labels are developing rapidly in the past several years. This study investigates how the average wage and number of stores affect the Chinese market's private label market share. More importantly, this paper examines the moderating effect of the average wage and the number of stores on the relationship between the private label market share and product assortment as well as the relationship between the private label market share and pricing. Data collected from a Chinese supply chain dyad is analyzed to study category management using hierarchical linear models. The results reveal that the average wage and the number of stores positively affect the private label market share. Furthermore, the average wage enhances the negative effect of the number of brands, weakens the negative effect of the private label price, weakens the positive effect of national brand price. Meanwhile, the number of stores enhances the positive effect of the SKU proliferation of private label, enhances the negative effects of the number of brands, and enhances the negative effect of the private label price. This study contributes to category management. Furthermore, the findings will be valuable to domestic and international grocery marketers and retailers operating private labels in China.     

Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Direct intervention or indirect support? The effects of cooperative control measures on farmers’ implementation of quality and safety standards

In recent years, labor-intensive agricultural products from developing countries have exhibited inferior performance in international trade due to quality and safety incidents, among which pesticide residue is a major issue. Aiming to improve food quality and safety in the context of cooperatives, we introduced three categories of control measures: outcome control, process control and social control. Based on the Pre-Harvest Interval Standard (PHIS), we selected three indices, farmers’ implementation rate of PHIS, absolute distance to PHIS and relative distance to PHIS, to evaluate appropriateness of pesticides use, reduction of pesticide residue and safety improvements of agricultural products. By using random sampling survey data, we empirically analyzed the marginal effects of control measures and their combinations on food quality and safety standards. The empirical results show that implementing process control, namely, unified production standards or supply of unified agricultural inputs, can comprehensively improve farmers’ implementation rate of PHIS, absolute distance and relative distance to PHIS respectively by 34.9%, 3.2 days and 46.0% on average. While the effects of outcome control (safety inspection) and social control (bonus-penalty incentive or training) are restricted to other measures. Therefore, we suggest cooperatives should take farmers’ features, implementation conditions and the effects of control measures into consideration in order to make a sustainable management plan for improving food quality and safety and enhancing competitiveness in international markets.     

A note on the long rate in factor models of the term structure

In this paper, we consider factor models of the term structure based on a Brownian filtration. We show that the existence of a nondeterministic long rate in a factor model of the term structure implies, as a consequence of the Dybvig–Ingersoll–Ross theorem, that the model has an equivalent representation in which one of the state variables is nondecreasing. For two‐dimensional factor models, we prove moreover that if the long rate is nondeterministic, the yield curve flattens out, and the factor process is asymptotically nondeterministic, then the term structure is unbounded. Finally, we provide an explicit example of a three‐dimensional affine factor model with a nondeterministic yet finite long rate in which the volatility of the factor process does not vanish over time.     

Error analysis of finite difference and Markov chain approximations for option pricing

Mijatovi? and Pistorius proposed an efficient Markov chain approximation method for pricing European and barrier options in general one‐dimensional Markovian models. However, sharp convergence rates of this method for realistic financial payoffs, which are nonsmooth, are rarely available. In this paper, we solve this problem for general one‐dimensional diffusion models, which play a fundamental role in financial applications. For such models, the Markov chain approximation method is equivalent to the method of lines using the central difference. Our analysis is based on the spectral representation of the exact solution and the approximate solution. By establishing the convergence rate for the eigenvalues and the eigenfunctions, we obtain sharp convergence rates for the transition density and the price of options with nonsmooth payoffs. In particular, we show that for call‐/put‐type payoffs, convergence is second order, while for digital‐type payoffs, convergence is generally only first order. Furthermore, we provide theoretical justification for two well‐known smoothing techniques that can restore second‐order convergence for digital‐type payoffs and explain oscillations observed in the convergence for options with nonsmooth payoffs. As an extension, we also establish sharp convergence rates for European options for a rich class of Markovian jump models constructed from diffusions via subordination. The theoretical estimates are confirmed using numerical examples.     

新制度经济学视角下的中国农地制度变迁：回顾与展望

研究目的:分析总结中国农地制度变迁的路径特征、决定条件和影响因素,并展望进一步研究方向。研究方法:文献分析法。研究结果:(1)改革开放以来的中国农地制度变迁呈现出一个较清晰的路径,即改革始终沿着市场取向、明晰产权、稳定地权的方向演进;(2)农地使用权制度改革的成功源于诱致性变迁与强制性变迁的结合,即弹性的中央政策制定、渐进式的地方实施以及具有学习效应的农户认知三者的有机互动,并体现出制度变迁的"共生演化"特征及其蕴含的"适应性效率";(3)新制度经济学是解释中国经济改革和农地制度变迁的有效工具,然而由于经典的新制度经济学本质上的静态性以及国家理论的缺乏,难以对现实世界的各类制度变迁给出一般化的解释。研究结论:基于经典的新制度经济学,进一步整合行为经济学、机制设计理论、演化博弈论等理论工具,超越"强制性—诱致性变迁"二分法,构建一个真正动态的制度变迁模型是一个重要研究方向。