基于ARIMA-GRACH模型的现货电价预测

通过建立ARIMA预测模型对现货电价进行预测,并对ARIMA模型存在的异方差问题通过GARCH模型进行修正。实证算例中,采用北欧四国电力市场数据,与ARIMA和灰色GM（1,1）模型进行比较,表明ARIMA—GARCH模型的预测精度更高,预测误差更小。     

基于灰色系统理论的我国石油消费对外依存度预测

在分析石油消费对外依存度与各个产业比重的关联度的基础之上,选取关联度最大的第二产业比重建立GM(1,1)模型,引入二阶软化算子调整第二产业比重数列,然后在此基础上建立石油消费对外依存度和第二产业比重的GM(1,2)模型。结果表明,我国石油消费对外依存度以年均1.51%的速度增长,到2018年将达60%。依据石油消费对外依存度指标等级分值表可知,届时我国石油安全处于高度危险的水平,因此,针对如何降低石油消费对外依存度给出了三点建议。     

Putting the New Keynesian DSGE Model to the Real‐Time Forecasting Test

The paper compares the quality of real‐time forecasts from a standard medium‐scale New Keynesian dynamic stochastic general equilibrium (DSGE) model to those from the Survey of Professional Forecasters (SPF) and DSGE‐VARs. It is shown that the DSGE model is relatively successful in forecasting the U.S. economy. This is especially true for forecasts conditional on SPF nowcasts, in which case the forecasting power of the DSGE turns out to be similar or better than that of the SPF for all the variables and horizons. An important weakness of the benchmark DSGE model is the poor absolute performance of its point forecasts and rather badly calibrated forecast densities.     

A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application

An artificial neural forecasting model is developed for air transport passenger analysis. It uses a preprocessing method that decomposes information to reveal relevant features from the data. It is found that neural processing outperforms the traditional econometric approach and offers generalization on time series behavior, even where there are only small samples.     

隧道岩溶施工处治方案探讨

文章针对岩溶产生的条件以及不同溶洞类型对隧道施工的影响进行概括性的阐述,同时对岩溶隧道超前探测预报技术进行了探讨,并对不同类型的溶洞,提出了相应的处治措施。     

浅谈工程施工成本的控制措施

随着企业标准化、规范化、程序化、信息化管理的不断完善和深入发展,企业的管理重心和管理责任已转向工程项目。企业是利润中心、项目是成本重心,这一管理理念的逐步形成,有效地改变和指导着工程成本的管理。在日益完善的市场竞争规则下,面对越来越激烈的市场竞争,施工企业,特别是建筑施工企业,应把精力更多地投入到控制工程成本管理的核心工作上,达到企业利益最大化。     

产业结构调整下的广州市技能人才需求预测分析

金融危机对广州的传统制造业产生了较大的冲击,调整产业结构,提升创新能力是当前摆脱危机,实现经济新一轮增长的重要途径。在此过程中,技能人才的供给是广州三次产业战略调整的直接制约因素。尤其随着广州产业结构调整节奏的逐步加快,其对技能人才的市场需求将与三次产业的产值结构相匹配而趋于刚性。因此,合理预测广州三次产业发展中对技能人才的需求,增加技能人才的供给能力,是广州产业结构调整中的重要问题。     

基于灰色理论的海西区域物流需求研究

物流产业的发展在"海西"经济区建设中发挥着重要作用。在当前物流产业统计指标体系尚未健全的情况下,本文运用灰色系统预测模型,对"海西"经济区建设的物流需求量进行预测,以此反映"海西"经济区物流需求的变化情况,为"海西"经济区物流产业的规划和制定相应物流政策提供依据。研究结果表明,GM(1,1)预测模型预测精度较高,效果较好。     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

Anticipating critical transitions of the housing market: new evidence from China

We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's metropolises. Building on the Log-Periodic Power Law Singularity (LPPLS) model of self-reinforcing feedback loops, we use the quantile regression calibration approach recently introduced by two of us to build confidence intervals and explore possible distinct scenarios. We propose to consolidate the quantile regressions into the arithmetic average of the quantile-based LPPLS Confidence indicator, which accounts for the robustness of the calibration with respect to bootstrapped residuals. We make three main contributions to the literature of real estate bubbles. First, we verify the validity of the arithmetic average of the quantile-based LPPLS Confidence indicator by studying the critical times of historical housing price bubbles in the U.S., Hong Kong, U.K. and Canada. Second, the LPPLS detection methods are applied to provide early warning signals of the housing markets in some metropolises in China. Third, we determine the possible turning points of the markets in Beijing, Shanghai, Shenzhen, Guangzhou, Tianjin and Chengdu and anticipate critical transitions of China's housing markets via our multi-scales and multi-quantiles analyses. Finally, given these projections performed in February 2017, the price trajectories from March 2017 to January 2018 that became available from the time of submission to the time of revision of the present article offer quite unique genuine out-of-sample tests of the performances of our indicators.