Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

信息分享对原始设备制造商与其合同制造商竞争的影响

研究了原始设备制造商的预测信息分享对一个原始设备制造商和一个与其同时有合作和竞争的合同制造商组成的供应链系统的影响,建立制造商间信息分享的模型,该模型包括一个原始设备制造商和一个合同制造商。研究发现,原始设备制造商关于市场潜在需求预测信息的分享对其预期利润是不利的,同时需求信息预测的精度对原始设备制造商信息分享的决策也有影响,原始设备制造商没有动机与其供应链成员进行信息分享,但信息分享使得供应链整体利润增加。最后,建立一个信息分享补偿机制分享供应链利润的增加量,以期通过信息分享补偿机制促使原始设备制造商有动机进行信息分享,从而实现其与合同制造商的“双赢”。     

The problem of employees' network centrality and supervisors' error in performance appraisal: A multilevel theory

This article reveals an unexplored paradox for HR managers: the centrality of an employee in the social network benefits performance but hampers performance appraisal because it affects supervisors' rating errors. Central employees can be erroneously rated high on performance even when they are not high performers because supervisors tend to overappraise their performance. A distinction is made between rating precision, which depends on supervisors' uncertainty regarding employees' performance, and rating accuracy, which depends on supervisors' bias in favor of employees. Employee centrality is posited to be beneficial to precision but deleterious to accuracy because it regulates the diffusion of positive information, status, and power, all of which distort supervisors' capacity and motivation to accurately appraise performance. It is then argued that rating errors caused by network centrality affect aggregate perceptions of justice in organizations. When employees are highly connected to each other in a dense network, organizations have a strong and positive justice climate. Yet when some employees are more central than others in a centralized network, organizations have a negative and weak justice climate. The article contributes to the literature because it identifies an unexplored dark side of network centrality and offers recommendations for HR managers to cope with its deleterious consequences and for scholars to study them.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

降水径流特征变化对月水量平衡模型模拟精度及参数的影响

为评估水文模型在变化环境下的可靠性,以两参数月水量平衡模型为例,采用Mann-Kendall法分析降水径流资料在MOPEX数据集中美国本土的104个典型流域的一致性,基于可变模糊理论评价降水径流资料一致性对两参数月水量平衡模型模拟精度的影响,并探讨降水径流资料一致性和流域的气候特征对模型参数的影响。结果表明:研究流域中,92.31%流域的降水或径流资料的一致性遭到破坏;通过可变模糊集评价模型模拟效果,发现降水、径流趋势变化均会削弱水文模型的模拟能力,其中降水一致性破坏是水文模型模拟能力减弱的主要原因,并且该方法能够准确地识别影响模拟能力的次要因素;模型参数C和参数SC均随着流域多年平均径流系数增大而增大,参数C反映了流域的湿润程度,参数SC表征了流域的调蓄能力。研究成果可为防洪、抗旱、水资源规划和管理提供技术支撑。     

单墩抗震性能指标与模型精细度的关联性研究

在考虑桥梁某些重要性能的抗震设计中,以钢筋和混凝土材料应变为基础衍生出的性能指标如曲率、位移等可以有效反映结构的非线性损伤状态。采用ADINA中弹塑性梁单元对单墩进行非线性时程分析,通过对采用不同积分段划分形式的模型分析,考察单墩抗震性能指标与有限元模型精细度的关系。计算结果表明,曲率对模型精度是敏感的,而位移、塑性铰对模型精度则不敏感,可以把塑性铰作为单墩抗震性能指标。     

山丘区小流域下垫面数据精度对HEC-HMS水文模拟的影响研究

山丘区小流域下垫面条件复杂多变,DEM分辨率及子流域划分水平对分布式水文模型模拟结果的影响有待深入研究。本文选取小流域应用广泛的HEC-HMS分布式水文模型,在河南省栾川流域设置四种DEM分辨率方案及四种子流域划分方案,分别提取不同方案下的流域水文特征参数进行对比,并分析两种类型的数据精度对纳什系数和峰值模拟的影响。研究结果表明,与大中流域相比,DEM分辨率和子流域划分对山丘区小流域空间参数和水文模拟结果的影响要更为显著。在实际山洪预警预报工作中,为保证模拟结果的稳定性和可靠性,应尽可能选择高精度下垫面数据精度。     

雷达测雨在水文学中的应用--影响预报精度的因素分析

雷达测雨的误差以及水文模型自身的结构和尺度问题等的复杂性，导致了水文预报的精度不理想、这在一定程度上阻碍了雷达测雨技术在水文学中的应用、文中在回顾雷达测雨技术在水文学中已发挥的重要作用的同时，着重对预报精度影响较大的若干因素进行探讨和分析，并就将来雷达测雨技术更好地应用于水文学的研究提出几点建议．     

改进粒子群优化算法在水文模型参数优化中的应用研究

引入改进的粒子群优化算法,对垂向混合产流模型计算参数进行优化,并对比参数优化前后水文模拟精度。研究结果表明：改进的粒子群优化算法模型可较快完成参数优化,相比于参数优化前,垂向混合产流模型年尺度模拟相对误差减少6.15%,模拟的过程确定性系数平均提高0.11;在次洪模拟中,模拟相对误差平均减少3.03%,模拟的洪水过程确定性系数平均提高0.19,水文模拟精度得到较大程度提高。研究成果对于区域水文模型参数优化提供参考价值。