互联网环境下基于消费者搜索的酒店入住率预测研究

互联网环境下消费者信息搜索反映了游客潜在的旅游需求。针对酒店入住率的非线性特征,以北京为例,构建BA-SVR@CSQ混合模型对北京星级酒店平均入住率进行预测,其中蝙蝠算法(Bat Algorithm,BA)用于优化SVR模型的自由参数,并利用2011年1月至2017年4月与北京旅游相关的消费者搜索数据(Consumer Search Queries,CSQ)构造SVR模型的输入集。12个月的预测结果表明,与基准模型相比,所构建预测方法能有效提高模型的预测精度,证实了网络搜索数据在酒店入住率预测中的重要价值,预测结果可为旅游相关部门的决策提供必要的参考。     

基于概率神经网络与多重分形的海温预测模型

文章提出了一种基于多重分形与概率神经网络相结合的海温预测方法.该方法利用多重分形方法将海温序列挖掘出多个蕴含海温变化信息的时间序列;利用多重分形计算得到的多个时间序列作为概率神经网络的输入因子建立预报模型;利用该预报方法对NINO综合区平均海温进行未来1～3个月的预报实验,结果表明:该方法能较好的实现NINO综合区平均海温的预测,这对厄尔尼诺/拉尼娜现象的监测和预报工作提供了一种新的方法.     

ARIMA模型在上海市全社会固定资产投资预测中的应用

本文采用自回归求积移动平均(ARIMA)法,对《上海市统计年鉴2002》提供的固定资产投资额资料进行了分析。结果显示,ARIMA(1,1,10)模型提供较准确的预测效果,可用于未来的预测,并为上海市全社会固定资产投资提供可靠依据。     

ARIMA模型在上海市全社会固定资产投资预测中的应用

文章采用自回归求积移动平均(ARIMA)法,对《上海市统计年鉴》(2002年)提供的固定资产投资额资料进行了分析。其结果显示:ARIMA(1,1,10)模型能提供较准确的预测效果,也可用于未来的预测,并为上海市全社会固定资产投资提供了可靠依据。     

四川省产成品存货组合预测研究

文章分别建立了四川省产成品存货的ARCH预测模型和GMDH变量自回归预测模型,然后利用它们的预测结果构建了ARCH-GMDH组合预测模型.将组合预测结果与产成品存货实际值以及ARCH、人工神经网络等单一模型的预测结果相比较,表明了所述方法的可行性和有效性,从而为产成品存货及其它宏观经济指标预测提供了一种思路.     

ARIMA模型在移动通信用户数预测中的应用

本文采用自回归求和移动平均模型(ARIMA(p,d,q)),对贵阳2002年7月到2005年6月的36个月忙时用户数据进行分析,结果显示,ARIMA(0,1,1)模型提供了较准确的预测结果,可用于对未来月份忙时的用户数预测。就此,可为交换设备的建设提供可靠的参考依据。     

ARIMA模型在基金指数预测中的应用

本文采用自回归移动平均模型(ARIMA),选取上证基金指数2005年6月1日至2006年5月31日共238个交易日的数据进行了实证分析,结果显示,与传统时间序列方法相比ARIAM(2,1,5)模型对上证基金指数具有更好的预测效果,可为投资者的决策提供较准确的依据。     

科技进步贡献率测算方法的改进

文章在采用索洛模型测算我国的科技进步贡献率时,对中心环节的测算方法做了改进.根据不同年份经济发展情况不同.把劳动、资本的产出弹性作为一个动态指标来测算;然后根据测算结果,利用二次移动平均预测法预测了未来十年我国的科技进步贡献率,预测结果与国家制定的中长期科技规划纲要所要求的目标较吻合,可以为未来十年我国科技进步水平提供参考.     

一种宏观经济计量模型的建立

宏观经济计量模型是经济预测工作的有效手段,为提高经济预测分析科学性提供了有力支撑。建模技术运用的合理性决定了模型的质量。本文就模型研制过程中涉及到的方法和技术手段进行探讨。     

基于自回归移动平均及支持向量机的中国棉花价格预测

文章通过对2008年至2011年间月度棉花价格数据进行分析,建立了基于自回归移动平均的棉花价格ARIMA(1,1,1)模型,结果显示,ARIMA(1,1,1)模型能够很好的模拟国内棉花价格,平均相对误差百分比低于4％,在ARIMA模型的基础上,对该模型残差建立支持向量机模型,将自回归移动平均模型与SVM模型组合对棉花价格进行了预测,比较预测结果,组合预测模型对自回归移动平均模型有一定改进.     

Forecasting time series of economic processes by model averaging across data frames of various lengths

This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes.     

Forecast Combination and Model Averaging Using Predictive Measures

We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting when uninformative priors on the model parameters are used and improves forecast performance. For the predictive likelihood we argue that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and in an application to forecasts of the Swedish inflation rate, where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.     

A quasi-Bayesian model averaging approach for conditional quantile models

The value at risk (VaR) is a risk measure that is widely used by financial institutions to allocate risk. VaR forecast estimation involves the evaluation of conditional quantiles based on the currently available information. Recent advances in VaR evaluation incorporate conditional variance into the quantile estimation, which yields the conditional autoregressive VaR (CAViaR) models. However, uncertainty with regard to model selection in CAViaR model estimators raises the issue of identifying the better quantile predictor via averaging. In this study, we propose a quasi-Bayesian model averaging method that generates combinations of conditional VaR estimators based on single CAViaR models. This approach provides us a basis for comparing single CAViaR models against averaged ones for their ability to forecast VaR. We illustrate this method using simulated and financial daily return data series. The results demonstrate significant findings with regard to the use of averaged conditional VaR estimates when forecasting quantile risk.     

基于一种贴近度的IOWA算子的优性组合预测模型

传统的组合预测模型中每一种单项预测方法在各个时点具有相同的加权系数,但实际上同一种单项预测方法在各个时点的预测精度有高有低,为了克服单项预测方法取固定权系数的缺陷,构建了基于一种贴近度的IOWA算子的变权系数的组合预测模型,并探讨模型的非劣性组合预测、优性组合预测存在性的充分条件,实例分析结果表明：该模型在预测效果评价指标体系中明显优于传统的组合预测方法。     

Forecasting comparisons using a hybrid ARFIMA and LRNN models

In this article, an autoregressive fractionally integrated moving average model (ARFIMA) and a layer recurrent neural network (LRNN) were combined to form a hybrid forecasting model. The hybrid model was applied on the daily crude oil production data of the Nigerian National Petroleum Corporation (NNPC) to forecast the daily crude oil production of the NNPC. The Bayesian model averaging technique was used to obtain a combined forecast from the two separate methods. A comparison was made between the hybrid model with standalone ARFIMA and LRNN methods in which the hybrid model produced better forecasting performance than the standalone methods.     

Forecast Combination Across Estimation Windows

In this article we consider combining forecasts generated from the same model but over different estimation windows. We develop theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error (RMSFE) compared with forecasts based on a single estimation window for all but the smallest breaks. An application to weekly returns on 20 equity index futures shows that averaging forecasts over estimation windows leads to a smaller RMSFE than some competing methods.     

When and how much do average forecasts improve predictive accuracy

A composite forecast combines two or more individual forecasts into a single estimate by way of a number of different averaging schemes. The easiest way to combine forecasts is through using a simple average. In this paper, the authors show that in many instances the simple average of individual forecasts approximates the optimal combining scheme. Results are expressed in terms of the probability that a composite forecast will improve upon an individual forecast.     

A model averaging approach for the ordered probit and nested logit models with applications

This paper considers model averaging for the ordered probit and nested logit models, which are widely used in empirical research. Within the frameworks of these models, we examine a range of model averaging methods, including the jackknife method, which is proved to have an optimal asymptotic property in this paper. We conduct a large-scale simulation study to examine the behaviour of these model averaging estimators in finite samples, and draw comparisons with model selection estimators. Our results show that while neither averaging nor selection is a consistently better strategy, model selection results in the poorest estimates far more frequently than averaging, and more often than not, averaging yields superior estimates. Among the averaging methods considered, the one based on a smoothed version of the Bayesian Information criterion frequently produces the most accurate estimates. In three real data applications, we demonstrate the usefulness of model averaging in mitigating problems associated with the ‘replication crisis’ that commonly arises with model selection.     

Model averaging for multivariate multiple regression models

The theories and applications of model averaging have been developed comprehensively in the past two decades. In this paper, we consider model averaging for multivariate multiple regression models. In order to make use of the correlation information of the dependent variables sufficiently, we propose a model averaging method based on Mahalanobis distance which is related to the correlation of the dependent variables. We prove the asymptotic optimality of the resulting Mahalanobis Mallows model averaging (MMMA) estimators under certain assumptions. In the simulation study, we show that the proposed MMMA estimators compare favourably with model averaging estimators based on AIC and BIC weights and the Mallows model averaging estimators from the single dependent variable regression models. We further apply our method to the real data on urbanization rate and the proportion of non-agricultural population in ethnic minority areas of China.     

非线性GARCH族的模型平均估计方法

本文考虑了非线性GARCH族的模型平均估计方法。在备选模型集合包含拥有不同模型结构的非线性GARCH族的情况下,本文构建了非线性GARCH族的模型平均估计量,并给出相应的权重选择准则。在一定正则条件下,本文证明上述模型平均估计量具有渐近最优性,即渐近实现真实最优的KL偏离度。蒙特卡洛模拟结果表明,在大部分情形下,本文提出的模型平均估计量取得了更小的相对KL偏离值。作为非线性GARCH族的模型平均估计方法的应用,本文对2016年6月1日至2017年6月1日上证指数的日波动率进行估计,与现有模型选择与模型平均方法相比较,本文模型平均估计方法具有更高的精度。