开滦电网电压的调整与控制

阐述了开滦电网运行中维护电网电压的稳定性的重要意义,分析比较了在现有的一次设备容量情况下,适合目前开滦电网的合理、经济的调节控制电压的技术方法.     

竞争环境下新能源汽车嵌入技术服务的定价策略

构建上游技术服务商和下游新能源制造商合作创新的两级供应链,考虑两个制造商的竞争关系和权力结构,研究不同竞争模式对产品定价、产品利润的影响,为技术服务商和新能源汽车制造商的决策提供参考。结果显示,技术服务商在同等条件下会提供技术给具有竞争关系的制造商以优化利润;制造商之间的权力结构会影响技术服务商的利润,并且在“权力对等”竞争模式(FLC)下的利润最大。从制造商角度,竞争强度和技术产出贡献度均会影响产品价格。在技术产出贡献度较小的情形下,垄断模式(M)下的产品定价最高,“制造商1领导”竞争模式(SLC)和“制造商2领导”竞争模式(TLC)下的定价较高,FLC模式下的定价最低;反之亦然。算例分析进一步指出,当技术产出贡献度较小时,M模式下的产品利润大于竞争模式(C)下的产品利润;反之亦然。此外,制造商之间的权力结构会影响C模式下的产品利润,因此,制造商在C模式的占优策略是通过FLC模式实施定价或在SLC和TLC模式下取得定价领导权。     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility

In a conditional predictive ability test framework, we investigate whether market factors influence the relative conditional predictive ability of realized measures (RMs) and implied volatility (IV), which is able to examine the asynchronism in their forecasting accuracy, and further analyze their unconditional forecasting performance for volatility forecast. Our results show that the asynchronism can be detected significantly and is strongly related to certain market factors, and the comparison between RMs and IV on average forecast performance is more efficient than previous studies. Finally, we use the factors to extend the empirical similarity (ES) approach for combination of forecasts derived from RMs and IV.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

Shift-contagion in energy markets and global crisis

This paper presents an analysis of shift-contagion in energy markets, testing whether linkages between returns in energy markets increase during crisis periods. The research presented herein demonstrates how common movement between energy markets increases due to (i) shift-contagion across energy markets, reflected by structural transmission of shocks across markets and (ii) larger common shocks operating through standard cross-market interdependences. A regime-switching model was developed to detect shift-contagion across energy markets. In the approach adopted herein, the occurrence of shift-contagion is endogenously estimated rather than being exogenously assigned. The results show that shift-contagion has been a major feature of energy markets over the last decade. Evidence is presented which demonstrates that the linkages between energy markets do not appear to be stable. These results are remarkably accurate for forecasting Brent and natural gas for horizons for up to 50 days. Conversely, for WTI (West Texas Intermediate oil) and coal, the model performs well only for forecasting very short horizons (up to 20 days). For all products, the model shows significant biases for long horizons.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

Enhancing survey‐based investment forecasts

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.     

面向突发事件的电网韧性能力评价及构建方法

近年来,我国对电力系统面对突发事件时的性能表现提出了更为严格的要求,利用韧性的概念构建电网系统能够实现应急全周期内避免损失.文章利用应急管理理论分阶段刻画韧性的特征,在描述突发事件下电网韧性能力的同时提出了一种定量评价电网韧性的方法,它侧重于电网在突发事件发生后随着时间的推移维持稳定、抵抗中断、恢复性能的调整能力:利用给出的韧性评价指数结合电网特有的包括运行模式、组件物理强度和网络拓扑结构在内的电网特征,针对性地描述了能够增强电网韧性的措施.最后举例选取N-1准则措施利用PSCAD软件进行仿真模拟,利用韧性评价指数说明N-1措施能够增强电网韧性的同时,验证了韧性评价指数的合理性.电网实例引用IEEE14节点模型.     

“翻转课堂”概念探析

随着信息技术的发展和普及,很多学校出现了新的教学模式和学习方法,“翻转课堂”作为教学模式的重要变革引起了社会各界的广泛关注,但仍有很多教师对“翻转课堂”存在误解。文章旨在探求该教学模式的概念产生过程及其本质,同时对其功能进行分析和反思,从而为现阶段我国的教育信息化进程提供助益。