组织信息化环境下知识流动对组织创造力的影响

通过解析知识流动和组织信息化环境的内部构成，探究知识溢出/知识扩散、高管支持/ICT应用对组织创造力的差异性影响，以及不同组织信息化环境对知识流动与组织创造力关系的调节作用。利用信息化程度较高的企业数据，使用层级回归分析进行实证检验。结果表明：组织创造力受知识流动的正向影响，知识扩散对其的正向影响更强；组织信息化正向影响组织创造力，相比ICT应用，高管支持对组织创造力的正向影响更强；高管支持在知识溢出/知识扩散与组织创造力之间的正向调节作用显著；ICT应用对知识扩散与组织创造力的强化作用显著强于对知识溢出与组织创造力之间的调节作用。     

金融支持国防知识产权成果转化问题及策略研究

国防知识产权成果转化应用是国防科技创新体系建设落地见效的关键环节，引入现代金融体系能够有效促进国防知识产权成果转化。目前我国国防知识产权转化工作在组织管理体制、工作运行体系、法规政策制度和信息化建设等方面初显成效，但仍面临整体转化形势不容乐观、金融支持体系尚未成熟等问题，根本原因在于国防知识产权与发明人割裂导致一般金融产品难以通过审核，使融资渠道狭窄；其保密性进一步加大了与金融机构间的信息不对称，使得金融资源配置效率低下；国防知识产权专用性较强，相应抬高了融资成本。为有效解决上述问题，提出3条改进措施，以实现对国防知识产权转化的金融支持，即①从顶层设计上优化解密脱密流程，明确发明人与知识产权的权属关系，细化财税优惠等支持措施；②加强政府财政税收政策支持，引导创新利用多元社会资本进入支持转化领域；③建立各主体参与的金融服务协调保障机制、监测评估体系，启动国防知识产权转化的金融支持试点工作，构建符合国情的国防知识产权转化金融支持体系。     

产军协同、政府科技支持与技术创新效率研究

运用空间数据分析方法，检验产军协同、政府科技支持与技术创新效率在我国省域间的分布格局。研究表明，三者均存在显著的正向空间相关性，即正向空间溢出效应，并且在不同地区形成局部空间集聚，存在高低非均衡的“俱乐部”现象。基于空间误差模型和空间滞后模型，实证检验产军协同、政府科技支持对技术创新效率的影响。结果发现，从全国和分地区看，产军协同、政府科技支持均能够显著促进技术创新效率提升，并且二者存在显著的交互作用，能够发挥彼此间的互补效应，进一步促进技术创新效率提升；不同地区产军协同、政府科技支持对技术创新效率的影响程度存在较大差异，西部地区产军协同、政府科技支持对技术创新效率的促进作用比东部和中部地区显著。     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

债务违约、社会责任与政府支持

转型期中国，地方国有企业作为地区经济发展的一股中坚力量，当其发生债务违约时，作为实际控制人的地方政府究竟是否愿意提供支持，会优先选择哪些企业提供支持，政府的支持行为是否会影响到信贷资源的配置效率呢?为得到验证，文章选取2007-2016年沪深A股地方国有上市企业作为研究样本，实证分析了地方国企发生债务违约后，地方政府的支持行为及其对信贷资源配置效率的影响。研究结论表明：第一，地方国企发生债务违约后，地方政府会提供更多的"支持"，且支持额度与债务违约额呈显著正相关关系；第二，债务违约前，承担了更多社会责任（环保投资、捐赠以及就业等）的地方国企，在债务违约后可相应地获得更多的地方政府支持；第三，违约企业，尤其是得到更多政府支持的违约企业可获得更多的信贷资源，而其经营业绩却往往更差。由此表明政府干预一定程度上降低了信贷资源配置效率。     

失独父母的社会支持体系现状及优化策略

基于社会支持理论,探讨失独父母的社会支持现状及其影响因素。2018年3-4月以多阶段分层整群抽样的方法,在我国西部某地级市抽取失独父母样本量108,分析失独父母的社会人口学特征、欧洲五维健康量表(EQ-5D)和社会支持评定量表(SSRS)等。失独父母的主观支持分、客观支持分、对支持的利用度和社会支持总分的中位得分低于国内常模及普通老年人的分数,线性回归分析结果显示,是否有第三代,是失独父母主观支持分、对支持的利用度和社会支持总分的影响因素;婚姻状况和月收入是失独父母客观支持分的影响因素。对失独父母须建立长期、固定的帮扶机制;政府应加大对患有大病、有第三代、退休金较少甚或没有的失独父母的经济帮扶力度;应夯实家庭医生重点签约对象的服务内容;须构建多种形式的特殊养老政策,引导失独父母自励互勉,构建多元化的社会支持体系,帮助失独群体早日融入社会。     

Impact of the Fast Track Land Reform Programme (FTLRP) on agricultural production: A tobacco success story in Zimbabwe?

The study ascertained the impact of the Fast Track Land Reform Programme (FTLRP) in Zimbabwe on tobacco production. The Chow Test, Vector Error Correction Model (VECM) and the Vector Error Granger-Causality Test were applied. The results reveal that there was a structural break in tobacco sales in the year 2000. Furthermore, in the long-run, area under tobacco production had a positive impact whilst number of tobacco producers had a negative impact on tobacco sold pre-FTLRP. Post-FTLRP, area of tobacco and number of tobacco producers had negative impact. In addition, the FTLRP induced an 8.94 % increase in the speed of adjustment in correcting the long-run equilibrium in tobacco sales. In the short-run, the FTLRP caused a percentage increase in the area of tobacco production and number of tobacco producers to induce a 0.65 % and 0.76 % increase in the tobacco sales, respectively. Area of tobacco production and number of tobacco producers Granger-caused tobacco sales in the pre-FTLRP period. Post-FTLRP, the number of tobacco growers Granger-caused tobacco sales. It is concluded that the FTLRP had an impact on tobacco sales, mainly through the number of tobacco growers. The study recommends the specialisation and training of the new farmers to improve productivity.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.