我国人均国内生产总值的时间序列分析(1952-2011)与静态预测(2012-2015)

国内生产总值是最重要的宏观经济指标,人均国内生产总值是衡量国家发展程度与人民生活水平的重要标志。党的十八大报告提出到2020年全面建成小康社会,实现GDP和人均收入比2010年翻一番的目标,因此对我国人均国内生产总值进行时间序列分析与静态预测对实现GDP总量翻番的目标具有重要意义。文章从国内生产总值和时间序列模型的基本理论出发,选取了自1952年至2011年人均GDP的60个样本数据,建立了以时间序列为基础的AR(1)模型,模型通过了EViews软件中的所有检验,模型的精度较高。文章利用AR(1)模型对2012年至2015年的人均GDP做出了预测,预测结果表明,至"十二五"期末,我国的名义人均GDP基本可实现比2010年翻一番的目标。     

基于ARIMA模型对上证指数的分析与预测

本文通过对2016年6月13日至2019年5月31日的上证指数收盘价进行时间序列分析,共获得724个样本数据,并对其构建ARIMA(3,1,3)模型。通过实证分析,得出以下结论:从短期看,ARIMA模型具有较好的预测能力,能够作为金融投资的一个决策工具;但从长期来看,其时效性相对较差。最后,在此分析基础上,为中国金融市场上的投资者以及政策制定者提出一些建议。     

长沙市PM_(2.5)污染特征及其影响因素分析

文章主要利用长沙市环境监测中心站的大气环境自动监测平台的监测数据,分析PM2.5质量浓度的污染特征。结果表明,长沙市的PM2.5质量浓度具有秋冬季高,春夏季低的特征;9个环境空气质量评价点中,火车新站PM2.5质量浓度最高,而经开区环保局的浓度最低。PM2.5质量浓度除受人类生产生活活动影响外,与当地气候条件(风速、降水量、温度、气压等)有关,一般情况下,风速越大,降水量越大,污染浓度越低;温度越高,污染浓度越低,但受逆温影响会出现相反情况。针对长沙市而言,2013年10月2日~10月12日期间,风速对PM2.5质量浓度的影响很小,相关性不大;受辐射逆温影响,污染物浓度较高,PM2.5质量浓度变化曲线与温度成负相关;PM2.5质量浓度变化曲线与相对湿度的呈正相关性,PM2.5质量浓度随着湿度的增大而增大。     

安徽省森林火灾预测及分析

针对当前安徽省森林防火工作的严峻形势,利用安徽省2004-2014年十年森林火灾的统计数据建立一元线性回归以及GM(1,1)模型对安徽省2015-2019五年森林火灾的发生进行预测,通过验差检验表明GM(1,1)预测值与实际值偏差较小,因此建立GM(1,1)模型对安徽省未来五年森林火灾次数、受害火场总面积和森林火灾其他损失折款进行预测,并结合安徽省当前林场保护政策对安徽省森林防火进行分析并提出建议。     

迪维西亚(Divisia)货币总量VAR模型的预测分析

本文针对最新编制的我国迪维西亚(Divisia)货币总量(指数)数据,运用向量自回归(VAR)模型预测实际产出和物价指数并与简单加总货币总量所得到的结果进行比较.结果显示,在预测我国实际产出值的VAR模型中,含有Divisia狭义货币(M1)总量的模型比含有简单加总货币总量M1的模型更精确;在预测我国的物价指数值的VAR模型中,含有Divisia广义货币(M2)总量的模型比含有简单加总货币总量M1和M2的模型更精确.     

基于GARCH模型的金融市场波动性分析与预测

以上证指数2004年1月2日至2014年12月28日交易日收盘价格的实际数据为样本,采取两种不同的波动率定义方式分别建立上证指数波动率的GARCH模型,并对两种波动率进行预测对比分析,实证研究结果表明以标准差定义的波动率建立GARCH(1,1)模型进行未来波动率预测的拟合效果较好。     

青岛市大气颗粒物PM2.5动态分析

基于青岛市主城区大气自动监测站实时发布的数据,分析颗粒物PM2.5浓度的时空变化规律,并对PM2.5与其它颗粒物之间的相关性和影响因素进行了分析。结果表明,PM2.5颗粒物浓度年际变化起伏不定;年内变化冬、春季浓度较高,夏、秋季浓度低。根据PM2.5与其它颗粒物之间的相关曲线看,PM2.5与SO_2、NO_2、O_3之间呈现显著正相关,它们浓度的升高会引起PM2.5浓度增大,其来源相同或相关。     

基于ARIMA模型的电影票房收入预测

电影行业是我国文化产业的核心,而电影票房收入影响着电影行业以及我国文化产业的持续发展,因此对电影票房的预测就显得尤为重要。文章以2013年3月至2018年12月全国电影票房收入为数据基础,建立了全国电影票房收入的自回归移动平均(ARIMA)模型,并对2019年1月至2019年10月我国电影票房收入进行了预测。     

灰色GM(2,1）模型在卷烟销量预测的应用

为研究红塔集团卷烟销量变化趋势,本文通过运用灰色系统理论,以"玉溪"品牌卷烟2008年至2017年销量作为灰色数,将其变换为生成数,建立GM(2,1)模型,给出二阶微分方程,并对微分方程中任意常数进行求解,确定微分方程通解,使卷烟销量的灰色信息白化,利用模型给出现实数据的生成规律。同时,根据给出模型对10年基础数据的真实值与模拟值进行了对比和检验,确定模型精度等级,并对2018年的卷烟销量进行预测,评估模型的实用性。检验结果显示,实际模型的残差检验、绝对关联度检验和均方差检验精度等级分别为三级、一级和一级,为合格模型,可以用于预测卷烟销量,预测结果显示,利用2008年至2017年"玉溪"品牌销量数据使用GM(2,1)建模得到的2018年销量预测值为138.32万箱,而2018年的真实销量为140.77万箱,相对误差为1.74%,预测结果精度较高,因此使用灰色GM(2,1)二阶模型研究短期内卷烟销量变化趋势有较高的实用价值。在实际应用上,文中GM(2,1)时间响应函数中任意常数的估计方法并不是最好的,更精确的任意常数的估计方法以及模型建立的基础数据取值的个数对模型精度的影响有待进一步研究。     

基于ARMA模型的我国经济增长率预测——三种基本形式预测效果的比较

文章基于我国1961-2016年GDP增长率的数据建立了AR模型、MA模型、ARMA模型,并据此预测了我国2012-2016年GDP的增长率.通过与2012-2016年实际GDP增长率的比较,发现:AR模型的预测效果是最差的,ARMA模型的预测效果要优于AR模型,MA模型的预测效果最符合当前我国经济的运行状况,但是波动幅度太大.最后,利用MA模型预测出2017年我国GDP增长率为7.32%.     

Expectations Formation and Business Cycle Fluctuations: An Empirical Analysis of Actual and Expected Output in UK Manufacturing, 1975–1996

Direct measures of expectations, derived from survey data, are used in a Vector Autoregressive (VAR) model of actual and expected output in eight industries in the UK manufacturing sector. No evidence is found with which to reject rationality in the derived expectations series when measurement error is appropriately taken into account. The VAR analysis illustrates the importance of intersectoral interactions and business confidence in explaining the time profile of industrial outputs, examines the mechanisms by which shocks are propagated across sectors and over time and investigates the relative importance of sectoral and aggregate shocks of different types.     

An overview of variance component estimation

Variance components estimation originated with estimating error variance in analysis of variance by equating error mean square to its expected value. This equating procedure was then extended to random effects models, first for balanced data (for which minimum variance properties were subsequently established) and later for unbalanced data. Unfortunately, this ANOVA methodology yields no optimum properties (other than unbiasedness) for estimation from unbalanced data. Today it is being replaced by maximum likelihood (ML) and restricted maximum likelihood (REML) based on normality assumptions and involving nonlinear equations that have to be solved numerically. There is also minimum norm quadratic unbiased estimation (MINQUE) which is closely related to REML but with fewer advantages.An invited paper for the ProbaStat '94 conference, Smolenice, Slovakia, May 30–June 3, 1994 Paper number BU-677 in the Biometrics Unit. Cornell University Ithaca NY     

基于便携式检测仪的空气PM2.5浓度监测与分析

本文介绍了PM2.5的基本概念以及空气质量的标准,在此基础上介绍了PM2.5检测装置和各种检测原理。检测了不同气候条件下,不同季节条件下,不同环境因素条件下空气中PM2.5浓度。并对检测结果进行了分析,得出了有效的结论,为PM2.5浓度的检测与分析理论打下了坚实的基础,具有十分重要的意义。     

Conditional assessment of the impact of a Hausman pretest on confidence intervals

In the analysis of clustered and longitudinal data, which includes a covariate that varies both between and within clusters, a Hausman pretest is commonly used to decide whether subsequent inference is made using the linear random intercept model or the fixed effects model. We assess the effect of this pretest on the coverage probability and expected length of a confidence interval for the slope, conditional on the observed values of the covariate. This assessment has the advantages that it (i) relates to the values of this covariate at hand, (ii) is valid irrespective of how this covariate is generated, (iii) uses exact finite sample results, and (iv) results in an assessment that is determined by the values of this covariate and only two unknown parameters. For two real data sets, our conditional analysis shows that the confidence interval constructed after a Hausman pretest should not be used.     

Approximately normal tests for equal predictive accuracy in nested models

Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods [West, K.D., 1996. Asymptotic inference about predictive ability. Econometrica 64, 1067–1084] to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken [2001. Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics 105, 85–110; 2005a. Evaluating direct multistep forecasts. Econometric Reviews 24, 369–404] to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure.     

Government debt and fiscal execution efficiency

Governments have used deficit policies in recent years, yet many still face fiscal debt problems. Thus, this research uses Range Directional Measure Dynamic Directional Distance Function model with negative data to explore the financial efficiency of local governments in Taiwan from 2011 to 2018. This article has three major contributions: (1) The research uses RDM Dynamic DDF model with negative data to solve the problem of negative values on input and output data and uses dynamic models to make up for the deficiencies of past research. (2) Due to the differences in regions, local governments have different fiscal budgets. Therefore, this article uses the Wilcoxon Test to explore the efficiency differences of local governments in different regions. (3) This article analyzes the impact of central subsidies and government deficits (debts) of local governments on fiscal efficiency, and discusses the efficiency of government fiscal execution. The results are as follows. (1) Seven counties and cities with the best efficiency, and seven local governments with poor efficiency. (2) The fiscal performances of outlying islands and eastern local governments are better than those of western local governments. (3) Kaohsiung City has the highest accumulated debt and Tainan City exhibits poor financial performance.     

Robust recurrent network model for intermittent time-series forecasting

This paper describes a deep-learning-based time-series forecasting method that was ranked third in the accuracy challenge of the M5 competition. We solved the problem using a deep-learning approach based on DeepAR, which is an auto-regressive recurrent network model conditioned on historical inputs. To address the intermittent and irregular characteristics of sales demand, we modified the training procedure of DeepAR; instead of using actual values for the historical inputs, our model uses values sampled from a trained distribution and feeds them to the network as past values. We obtained the final result using an ensemble of multiple models to make a robust and stable prediction. To appropriately select a model for the ensemble, each model was evaluated using the average weighted root mean squared scaled error, calculated for all levels of a wide range of past periods.     

基于系统动力学的房产税政策仿真研究

为了科学、有效地分析房产税对房地产市场的调控作用,提高房地产政策的合理性与前瞻性,有必要建立商品住宅系统政策仿真模型,深入分析住宅系统内各因素之间的反馈关系。基于建立的住宅仿真模型,实现对房产税政策的模拟实验,得出房产税政策对住宅需求市场以及住宅价格的影响程度。本文以昆明市房地产市场作为研究样本,利用仿真系统进行了实证分析。实证结果表明,房产税对住宅增量有抑制作用,但短期内对住宅价格影响程度有限。     

西安市空气中PM2.5的影响因素

文章以西安市2013年的空气数据为研究对象,分析了PM2.5与每个因素的相关性,并进行了PM2.5的空气动力学分析,发现主要影响西安市空气中PM2.5的因素是温度和天气状况。     

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.