Asymmetry,Complementarities, and State Dependence in Federal Reserve Forecasts

Forecasts are a central component of policymaking; the Federal Reserve's forecasts are published in a document called the Greenbook. Previous studies of the Greenbook's inflation forecasts have found them to be rationalizable but asymmetric if considering particular subperiods, for example, before and after the Volcker appointment. In these papers, forecasts are analyzed in isolation, assuming policymakers value them independently. We analyze the Greenbook forecasts in a framework in which the forecast errors for different variables are allowed to interact. We find that allowing the losses to interact makes the unemployment forecasts virtually symmetric, the output forecasts symmetric prior to the Volcker appointment, and the inflation forecasts symmetric after the onset of the Great Moderation.     

Economic policy uncertainty and analysts’ forecast characteristics

We examine the influence of economic policy uncertainty (EPU) on the characteristics of analysts’ earnings forecasts over a thirty-year period, spanning a wide variety of political and economic conditions. Motivated by both theory and empirical evidence that suggest a decline in the quality of the information environment for firms as EPU increases, we establish that analysts’ forecast errors increase with EPU, as does the degree of forecast dispersion. Increased error and dispersion persist after controlling for several competing sources of economy-wide uncertainty. Cross sectional analysis exploring heterogeneity in forecast quality across both analyst and firm characteristics establishes that forecast error and dispersion increase with EPU across a broad spectrum of firms and levels of analyst expertise. We control for analysts’ experience overall and the years spent covering a particular industry and firm. Five alternative methods for classifying firms as policy sensitive versus policy neutral provide consistent evidence that analyst forecast errors and dispersion increase with EPU, even for firms not deemed to be particularly sensitive to policy.     

黔东南山地生态农业发展评价及预测研究

[目的]对2012~2016年黔东南州山地生态农业发展做出评价,并预测其2018~2027年的发展情况,以期了解黔东南地区农业可持续发展能力及未来趋势的变化规律。[方法]以黔东南及贵州《2013~2017年统计年鉴》为数据来源,通过构建评价指标体系,利用层次分析法及加权评分法对2012~2016年山地生态农业发展做出评价;并运用灰色模型,借助Matlab工具,对黔东南山地生态农业发展做出预测。[结果]2010~2016年黔东南州山地生态农业的经济、生态、社会效益处于发展上升阶段,协调度良好,但生态效益发展最低,其中2015年经济、社会、生态系统可持续发展良好,但由于持续保护,2016年出现生态保护过度,经济和综合效益下降现象,因此发展模式有待于向发展经济和社会效益方面倾斜;黔东南州山地生态农业在2018~2027年将会处于发展上升阶段,但其发展缓慢,仍存在一定限制因素。[结论]政府应积极调整农业产业结构,转变农业发展思路,可将部分生态农业与旅游相结合,发挥区位特色的同时,提升品牌的知名度。加大力度引导农业园区和重点产业的发展,推进农业农村改革试点的运行,加大新型农人的培育,促进高效、现代、特色生态农业的发展。     

扬州市生态农业发展评价及预测研究

［目的］农业是国民经济的基础产业，发展生态农业是实现农业经济稳步增长的重要途径，也是我国发展现代农业的战略选择。对生态农业的现状进行评估，以期以此为依据为今后的发展提供可行性建议。［方法］文章研究从经济效益、社会效益和生态效益3个方面构建了包含14个指标在内的扬州市生态农业发展评价指标体系，并采用熵权法和加权法综合评定2011—2016年该市生态农业发展的经济效益、社会效益和生态效益及综合效益得分，在此基础上采用GM（1， 1）灰色模型对2018—2027年扬州市生态农业的发展程度进行了预测。［结果］2011—2016年扬州市生态农业发展的社会效益呈持续增长态势， 2011—2015年经济效益呈上升趋势， 2016年经济效益明显下降。生态效益分别在2012年和2016年出现两次波动。总体来看， 2012—2015年扬州市生态农业发展的综合效益逐年递增， 2016年由于经济效益的急剧降低，导致综合效益下降。2018—2027年该市生态农业的发展水平一直保持可持续状态，且发展度在逐年递增。［结论］扬州市在今后的发展过程中应注重转变生产方式，积极调整农业生产模式，努力实现农产品的集约化生产。注重环境保护，实现化肥农药使用量零增长，达到经济效益与社会效益和生态效益协调增长。     

Does Integrated Reporting Matter to the Capital Market?

Integrated reporting (<IR>) is an emerging international corporate reporting initiative to address limitations to extant corporate reporting approaches, which are commonly criticized for being both voluminous and disjointed. While <IR> is gaining in popularity, current momentum has been limited due to a lack of clear evidence of its benefits. Utilizing the most suitable setting currently available, being discretionary disclosures made by listed companies on the Johannesburg Stock Exchange, this study provides evidence that analyst forecast error reduces as a company's level of alignment with the <IR> framework increases. Further, the improved alignment is associated with a subsequent reduction in the cost of equity capital for certain reporting companies. The results are obtained after controlling for factors relating to financial transparency and the issuance of standalone non‐financial reports, which suggests that <IR> is providing incrementally useful information to the capital market over and above existing reporting mechanisms.     

基于Stacking模型融合的店铺销量预测

为了实现企业产品销量预估,提高生产供应的准确性与效率,提出了基于Stacking模型的融合算法进行销量预测。算法设计了两层堆叠的模型结构,初级学习器采用随机森林、支持向量回归、差分整合移动平均自回归、轻量级梯度提升机器和门控循环单元5种单模型,将分类与回归树作为次级学习器构成Stacking融合模型,并对数据进行了预测。预测结果显示,使用Stacking模型融合后得到了较好的预测结果,比单模型中效果最好的模型的均方根误差更小,平均绝对误差更小,决定系数值更大,表明Stacking融合模型的预测准确率更高。所设计模型可用于对企业店铺的产品销量进行预测,帮助企业更好地安排生产、营销活动,为减少库存、缩短生产销售周期提供数据支持,对企业生产决策有一定的参考价值。     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

Target price forecasts: The roles of the 52-week high price and recent investor sentiment

This paper reveals that in addition to fundamental factors, the 52-week high price and recent investor sentiment play an important role in analysts’ target price formation. Analysts’ forecasts of short-term earnings and long-term earnings growth are shown to be important explanatory variables for target prices; equally, the 52-week high price and recent investor sentiment are also shown to explain target price levels and especially target price biases. Our analysis additionally reveals that analysts place greater weight on these two non-fundamental factors in settings with greater task complexity and to some extent in those with greater resource constraints. Conversely, on balance, the results suggest that this increased reliance does not translate into an increased impact per unit of each non-fundamental factor on forecast bias. Finally, our results show that target prices are useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies. However, in an internally consistent fashion, the informativeness of target prices for future returns is significantly reduced when greater weight is placed on either the 52-week high or recent investor sentiment in the target price formation process.     

Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match

Team QUINKAN competed in the GEFCom2017 final match of hierarchical probabilistic load forecasting by adopting the quantile regression method using the R package quantreg. The weather stations were clustered into 11 groups, from which an optimal one was chosen for each load meter using the boosting method. The load meter records were cleaned and/or supplemented by various methods in order to secure robust quantile predictions. The variation in the regression formulas was kept as small as possible by introducing measures for suppressing prediction instability, although special formulas were employed for loading meters that were of an industrial nature. Several procedures were applied to help improve the accuracy, such as the smoothing of season transitions, coarse graining of the relative humidity, the use of load-oriented day-type definition, the averaging of weather data, and outlier removal.     

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.