数字技术采纳、社会网络拓展与农户共同富裕

数字技术不仅是农户生产生活的重要工具,而且是推进农户共同富裕的重要途径。现有研究对共同富裕的测度多从宏观视角出发,测度区域和国家的总体共同富裕水平,鲜有研究从微观层面探究增进农户共同富裕的问题。文章基于2019年中国社会状况综合调查(CSS2019)数据,对7031份农户样本的共同富裕水平进行刻画,运用Oprobit模型和中介效应模型实证检验数字技术采纳对农户共同富裕的影响及作用机制。研究发现:数字技术采纳显著提升了农户共同富裕水平,在更换变量、采取工具变量法估计与PSM匹配等检验后,结论仍然稳健。机制分析发现,数字技术采纳通过拓展社会网络促进农户共同富裕,其中,社会网络规模和社会网络强度是其主要传导机制。异质性分析发现,数字技术采纳对低收入组农户的共同富裕促进效应强于高收入组,有助于缓解农村内部贫富差距,发挥着促进"增富"及"共享"的分配效应;采用数字技术进行浏览信息、休闲娱乐、商务工作和投资理财对农户共同富裕发挥显著的促进作用,其中采用数字技术进行商务工作,对农户共同富裕水平的提升效应最大。可见,应以实施乡村建设行动为契机,完善农村数字硬件设施和软件基础,提升农户数字技术采纳能力,共享数字化发展红利,实现共同富裕。     

Dynamic spillovers and linkages between gold,crude oil,S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switch between positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.     

Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage

Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.     

Lending relationships when creditors are in control

Violations of financial covenants shift control rights to lenders. When borrowers have lending relationships with these lenders in control, they experience not only smaller declines in investment, but also lesser deteriorations in both firm survival probabilities and in sales. These effects are largely driven by opaque borrowers without any credit ratings. They are present where lending relationships existed already before loan issuance (ex-ante), but also where a contractual relationship without pre-issuance interaction is more mature (ex-post). Surprisingly, there is no evidence of any “dark side” of lending relationships when creditors are in control, such as an increase in interest expenses or a lesser degree of financial discipline.     

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach

Macroeconomic data are an important piece of information in decision making for both the public and private sectors in Thailand. However, the release of key macroeconomic data, usually in a lower frequency such as quarterly, is not always in a timely manner. Using the higher frequency data such as monthly and daily to analyze or forecast the lower frequency data can mitigate the release timing effect. This study applies the mixed-frequency data approach to analyze and forecast Thai key macroeconomic data. The mixed data sampling regressions with various specifications are employed and implemented through some macroeconomic data such as gross domestic product and inflation. The results show that in most cases the mixed-frequency models outperform the autoregressive integrated moving average model, which we used as the benchmark model, even during the COVID-19 period. Some policy implications can also be drawn from the analysis.     

Sampling error and the joint estimation of imputation credit value and cash dividend value

The value of imputation credits can only be estimated jointly with the value of cash dividends. We show that random variation across samples leads to estimates of credit value that move in the opposite direction to estimates of cash value. Derivative prices suggest a value for credits of 0.01 to 0.20 (0.01 to 0.07 if cash is worth 0.94, and 0.13 to 0.20 if cash is worth 0.87). Ex-dividend prices suggest a value for credits of 0.23 to 0.46 (0.23 to 0.36 if cash is worth 0.85, and 0.33 to 0.46 if cash is worth 0.75).     

中国研发人员性别结构分布演变、关联影响与差异来源——基于半参数核回归与Oaxaca分解的研究

激发女性研发人员创造活力，对于中国创新经济发展具有重要意义。然而，科研领域性别结构失衡是长期以来科学共同体内外所面临的问题。基于2010-2019年中国内地30个省份面板数据，围绕企业、研发机构、高校等创新主体，在对创新全要素生产率指数（以下简称创新TFP）进行测度的基础上，运用核密度估计对三大创新主体的性别结构、创新TFP分布的演进进行描绘。其次，采用半参数核回归法探析科研人员性别结构与创新投入、产出和创新TFP间的关联。最后，选用Oaxaca分解法探究STEM研究生性别差异对R&D人员性别差异的解释程度。结果表明：相比研发机构和高校，企业研发人员性别失衡更为严重，相比人口大省、经济发展落后地区，发达地区对研发人员性别结构的“兜底作用”更显著；创新投入、产出与性别结构之间存在非对称关系，在研发人员女性占比越高的地区，区域全要素生产率指数越高；STEM领域研究生数量上的性别差异可以解释约6成的研发人员性别差异，且解释程度呈逐渐增提升势。     

期货市场投资者行为与价格波动关系研究

通过从上海期货交易所获取数据,使用向量自回归检验了两种类型投资者的行为和价格波动性之间的关系.实证检验结果表明:(1)不同期货品种市场上的投机行为都会加剧价格波动性,而不同价格波动率度量方法下,套期保值行为对价格波动所产生的Granger显著影响只出现在某些期货品种市场上;(2)市场价格波动对套期保值行为没有显著的影响,而市场价格波动对投机者行为的影响则随着波动率的度量方法不同而不同.研究结论对于指导我国期货市场改善投资者结构、促进期货市场发展具有积极的意义.     

Multivariate and geospatial analysis of technology utilization in US counties

We examine geographic patterns and socio-economic and social capital correlates of the adoption and utilization of information and communication technologies (ICTs) in 3,108 counties of the United States. Access and use of ICTs are found to vary significantly among counties in metropolitan, micropolitan, and rural parts of the country. Clusters of high, moderate, and low ICT utilization counties are characterized by geodemographic and socio-economic attributes. Regression findings indicate that young dependency ratio, college education, working age population, and race and ethnic variables are major correlates of ICT use. We analyze and explain novel findings on associations of income, ethnicity, and social capital variables with ICT usage in light of the technology-use landscape in the US. Spatial bias and large sample size fallacy issues are addressed and policy recommendations to remediate the digital divide in US counties are provided.