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。     

Real income convergence and the patterns of financial integration in the EU

We analyse real income convergence among the EU28 countries throughout 1995–2017 and the relationship with the convergence patterns of financial systems. We apply the nonlinear latent factor model of Phillips and Sul (2007, 2009) to real incomes and the IMF financial development indices for financial markets and financial institutions (Svirydzenka, 2016), and identify convergence clubs endogenously. We have several results. First, income disparities narrowed significantly over the last twenty years; yet, the growth convergence process lost momentum triggered by the global financial crisis and countries legacies shaped up asymmetries that have crystallised. Second, countries' financial systems exhibit high fragmentation, especially for financial markets, with the new EU member states at a lower financial development, confirming the existence of a two-tier Europe. Overall, the convergence patterns for real incomes and financial development are strongly correlated. Finally, the financial structure matters and market-oriented economies feature higher long-run growth, indicating the need to implement pan-European policy actions that increase the opportunities of risk diversification, enhance capital raising and channel large-scale financing to firms.     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Exploring the links among corporate social responsibility,reputation, and performance from a multi-dimensional perspective

This study examines the links among corporate social responsibility (CSR), reputation, and performance in hotel companies from a multidimensional perspective. Data were collected from 322 hotels in China and partial least squares equation modelling (PLS-SEM) was employed for a causal-predictive analysis. Study results reveal that CSR practices influence hotel reputation as seen from both international and local perspectives. CSR and reputation influence performance dimensions (accounting-based performance, market-based performance, and non-financial performance) differently. The research findings offer specific theoretical and practical implications for hotel managers.     

长江经济带人才集聚水平测度及时空演变研究——基于价值链视角

基于价值链视角，构建涵盖人才资源在区域空间层面集聚生成、地区配置、效能产出3个维度在内的区域人才集聚水平评价指标体系，采用专家咨询AHP-信息熵组合赋权法构建综合评价模型和耦合协调度模型，对2010—2018年长江经济带沿线省市人才集聚水平进行测度与时空演化特征分析。结果表明，就长江经济带整体而言，考察期内地区人才集聚水平总体呈现逐年上升态势，但仍处于低水平状态；沿线省市人才集聚水平差异显著，缩小趋势渐缓，整体呈现局部高水平、全局低效率的不利境况；各省市人才集聚水平关键维度的耦合协调度整体呈显著波动递增态势，但内部耦合协调度存在不平衡发展问题，影响地区人才集聚水平整体提升。为提升长江经济带人才集聚水平，推进沿线地区协调发展，需强化区域人才合作机制，采取差别化精准策略破解沿线地区人才集聚低水平、不协调的短板因素。     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Exploring the representativeness of the M5 competition data

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field to identify best practices and highlight their practical implications. However, can the findings of the M5 competition be generalized and exploited by retail firms to better support their decisions and operation? This depends on the extent to which M5 data is sufficiently similar to unit sales data of retailers operating in different regions selling different product types and considering different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporación Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest only minor discrepancies between the examined data sets, supporting the representativeness of the M5 data.     

How do valuations impact outcomes of asset sales with heterogeneous bidders?

Differences among bidder type-specific outcomes of asset sales are theoretically related to differences in bidders’ valuations and participation. The lead application to quantify these relations is takeover auctions: bidders are classified into strategic and financial, and bids are available. I structurally estimate valuations from all bids. The positive difference in premiums between strategic and financial acquirers is driven by the difference in dispersions of valuations (e.g., strategic bidders’ synergies are more dispersed) and the set of auction participants. The difference in average valuations is relatively unimportant. My approach can help explain outcomes of asset sales, even in settings with limited bidder data.     

Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.