指数平滑预测法及其在经济预测中的应用

以1978—2008年的某省职工年平均工资作为样本数据、2009—2010年的数据作为模型检验数据,建立基于时间序列分析的指数平滑预测模型。检验结果表明,指数平滑预测模型对2009年和2010年的预测值与检验样本的实际值之间的相对误差很小（仅为0.015032和0.02207）,预测结果理想。随后,以此模型预测时间序列2012—2015年职工平均工资数据。     

Asset price volatility and trading volume with rational beliefs

Summary. This paper develops a model of speculative trading in a large economy with a continuum of investors. In our model the investors are assumed to have diverse beliefs which are rational in the sense of being compatible with observed data. We demonstrate the existence of price amplification effects and show that the equilibrium prices can be higher or lower than the rational expectation equilibrium price. It is also shown that trading volume is positively related to the directions of price changes. Moreover, we study how asset price volatility and trading volume are influenced by belief structures, short selling constraints and the amount of fund available for investment.Received: 23 January 2003, Revised: 30 April 2003, JEL Classification Numbers: D84, G12.We are grateful to Professors Mordecai Kurz, Kenneth Arrow, Kenneth Judd, Carsten Nielsen, Maurizio Motolese, Mark Garmaise, Jean-Michel Grandmont, Peter Hammond, Karl Shell, Jan Werner and participants of the Society for the Advancement of Economic Theory (SAET) Conference and Stanford Institute of Theoretical Economics (SITE) Conference for many helpful suggestions. Correspondence to: H.-M. Wu     

Does VIX or volume improve GARCH volatility forecasts?

This article considers whether the inclusion of two additional variables can improve volatility forecasts over a standard GARCH-based model. We consider three alternative ways of incorporating the volatility index (VIX) and trading volume as exogenous variables within a selection of GARCH models. We are particularly interested in whether these variables have additional incremental forecast power over and above the baseline GARCH specification. Our results suggest that both the VIX and volume do provide some additional forecast power, and this is generally improved when considering both of these series jointly in the model. However, while the results may be statistically significant the gain is marginal and the coefficient values small. Moreover, in a horse race exercise VIX does not outperform the GARCH approach. In answering the question of whether VIX produces better forecasts than the GARCH model, then the answer is no, but the informational content of VIX cannot be ignored and should be incorporated into forecast regressions.     

基于最优组合预测模型的港口集装箱吞吐量预测

根据港口集装箱吞吐量非线性增长等特点,建立了三次指数平滑预测模型、灰色系统预测模型及BP神经网络预测模型等单项预测模型。鉴于单项预测模型的局限性,提出了以测试数据的预测误差绝对值加权和最小为最优化准则的最优组合预测模型,采用线性规划的方法确定最优组合的权系数。最后,给出一个实例进行应用和分析。     

我国海洋产业增加值的组合预测分析

近年来,我国海洋产业增加值大幅度地提高。鉴于单项预测模型的局限性,本文运用线性规划的方法赋予合理的权重,将时间趋势模型和指数平滑模型加权组合,采用组合预测的方法对我国未来几年的海洋产业增加值进行了预测。     

中国碳排放试点市场碳交易价格分析及预测

对试点市场碳价格分析结果表明:①试点市场碳价格平均水平相差较大,各市场数据均出现尖峰厚尾、波动率集聚、多重分形特征;②试点市场月均价分析过程中,发现新的碳排放市场的建立会对各个碳市场交易价格有提升作用,免费碳排放配额比例的适当调整有利于碳排放配额交易价格下降,碳排放市场核算与核查体系的逐步完善会使碳排放配额交易价格趋于平稳。本文采用马尔科夫转换多重分形模型对碳价格进行预测,得出了准确度较高的结果。     

Do misalignments predict aggregated stock-market volatility?

This paper considers forecasting regressions of “realized volatility” on a misalignment measure. Results show that this misalignment measure is useful to predict in and out-of-sample stock-market volatility at monthly horizons. The analysis also suggests a threshold effect.     

"十一五"我国旅游业吸纳劳动力的数量分析与预测

本文在统计数据的支持下,建立了旅游收入、旅游客源与旅游直接从业人员的相关模型;利用EXCEL模型分析预测了2005-2010年我国旅游业吸纳劳动力的数量,从而说明发展旅游业是解决我国就业问题重要而有效的手段。     

中国碳交易二级市场有效性研究

选取北京、上海、广东、湖北碳交易市场自成立至2017年3月31日的收盘价数据,通过对日收益序列数据的分析,运用一阶自回归过程调整日收益序列以消除淡薄交易市场效应,之后综合运用检验性逐渐增强的4个方差比检验,判断4个碳交易市场的弱式有效性。研究结果表明：①国内碳交易市场属于淡薄交易市场;②市场中的价格信息堆积,信息透明度较差;③碳交易市场投资风险较大;④碳配额持有期不同,市场有效性具有差异,且具有阶段性特点;⑤北京碳交易二级市场属于弱式无效市场,上海、广东碳交易市场虽属于弱式无效市场,但随着碳额持有期增加,市场的弱式有效不断加强,湖北碳交易已经达到了弱式有效水平。最后,基于研究结论对如何加强中国碳交易二级市场有效性提出4点建议。     

秦皇岛港煤炭吞吐量预测研究

秦皇岛港是我国北方以煤炭出口为主的重要物流枢纽.文章分析了秦皇岛港煤炭吞吐量与时间序列的二次曲线变化趋势,在此基础上采用1991～2009年秦皇岛港历史煤炭吞吐量数据,通过三次指数平滑法对2010年煤炭吐量进行预测,为把秦皇岛港打造“物流港”提供了理论依据.     

基于IOWA算子的我国固定资产投资的组合预测

以我国固定资产投资额1991-2010年的统计数据作为样本区间,分别通过三种单项预测方法进行预测,建立了基于IOWA算子的组合预测模型,计算出相应的预测值和预测精度。通过建立相应的评价指标体系,预测了未来五年我国固定资产投资额,预测结果与实际较接近,符合我国政策趋势。     

Modelling exchange rate volatility with random level shifts

Recent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward biased. In this article, we directly estimate a random level shift model to the logarithm of the absolute returns of five exchange rates series, in order to assess whether random level shifts (RLSs) can explain this long memory property. Our results show that there are few level shifts for the five series, but once they are taken into account the long memory property of the series disappears. We also provide out-of-sample forecasting comparisons, which show that, in most cases, the RLS model outperforms popular models in forecasting volatility. We further support our results using a variety of robustness checks.     

Forecasting realized volatility of crude oil futures with equity market uncertainty

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market.