Vector Autoregressive （VAR） Modeling and Projection of DSE

In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.

PC-VAR Estimation of Vector Autoregressive Models

In this paper PC-VAR estimation of vector autoregressive models (VAR) is proposed. The estimation strategy successfully lessens the curse of dimensionality affecting VAR models, when estimated using sample sizes typically available in quarterly studies. The procedure involves a dynamic regression using a subset of principal components extracted from a vector time series, and the recovery of the implied unrestricted VAR parameter estimates by solving a set of linear constraints. PC-VAR and OLS estimation of unrestricted VAR models show the same asymptotic properties. Monte Carlo results strongly support PC-VAR estimation, yielding gains, in terms of both lower bias and higher efficiency, relatively to OLS estimation of high dimensional unrestricted VAR models in small samples. Guidance for the selection of the number of components to be used in empirical studies is provided.

Impact of Inflation, Dollar Exchange Rate and Interest Rate on Red Meat Production in Turkey： Vector Autoregressive （VAR） Analysis

In this study, impact of inflation （WPI--Wholesale Price Index）, exchange rate, and interest rate on the production of red meat in Turkey was examined using the vector autoregressive （VAR） model. The model consisting of variables of dollar exchange rate, inflation rate, interest rate, beef, buffalo meat, mutton, and goat meat production amounts has been estimated for the period from 1981 to 2014. It has been detected that there is a tie among the dollar exchange rate, inflation rate, interest rate, and the amount of red meat production in Turkey. In order to determine the direction of this relation, Granger causality test was conducted. A one-way causal relation has been observed between： the goat meat production and dollar exchange rate; the buffalo meat production and the mutton production; and the beef production and the mutton production. To interpret VAR model, the impulse response function and variance decomposition analysis was used. As a result of variance decomposition, it has been detected that explanatory power of changes in the variance of dollar exchange rate, inflation rate, and interest rate in goat meat production amount is more than explanatory power of changes in the variances of mutton, beef, and buffalo meat variables.

Utilizing the Vector Autoregression Model (VAR) for Short-Term Solar Irradiance Forecasting

Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.

Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks

In the paper, a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples.

Short Term Forecasting Performances of Classical VAR and Sims-Zha Bayesian VAR Models for Time Series with Collinear Variables and Correlated Error Terms

Forecasts can either be short term, medium term or long term. In this work we considered short term forecast because of the problem of limited data or time series data that is often encounter in time series analysis. This simulation study considered the performances of the classical VAR and Sims-Zha Bayesian VAR for short term series at different levels of collinearity and correlated error terms. The results from 10,000 iteration revealed that the BVAR models are excellent for time series length of T=8 for all levels of collinearity while the classical VAR is effective for time series length of T=16 for all collinearity levels except when ρ = -0.9 and ρ = -0.95. We therefore recommended that for effective short term forecasting, the time series length, forecasting horizon and the collinearity level should be considered.

A Simulation Study on the Performances of Classical Var and Sims-Zha Bayesian Var Models in the Presence of Autocorrelated Errors

It is well known that a high degree of positive dependency among the errors generally leads to 1) serious underestimation of standard errors for regression coefficients;2) prediction intervals that are excessively wide. This paper set out to study the performances of classical VAR and Sims-Zha Bayesian VAR models in the presence of autocorrelated errors. Autocorrelation levels of (-0.99, -0.95, -0.9, -0.85, -0.8, 0.8, 0.85, 0.9, 0.95, 0.99) were considered for short term (T = 8, 16);medium term (T = 32, 64) and long term (T = 128, 256). The results from 10,000 simulation revealed that BVAR model with loose prior is suitable for negative autocorrelations and BVAR model with tight prior is suitable for positive autocorrelations in the short term. While for medium term, the BVAR model with loose prior is suitable for the autocorrelation levels considered except in few cases. Lastly, for long term, the classical VAR is suitable for all the autocorrelation levels considered except in some cases where the BVAR models are preferred. This work therefore concludes that the performance of the classical VAR and Sims-Zha Bayesian VAR varies in terms of the autocorrelation levels and the time series lengths.

On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms

In time series literature, many authors have found out that multicollinearity and autocorrelation usually afflict time series data. In this paper, we compare the performances of classical VAR and Sims-Zha Bayesian VAR models with quadratic decay on bivariate time series data jointly influenced by collinearity and autocorrelation. We simulate bivariate time series data for different collinearity levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) and autocorrelation levels (﹣0.99, ﹣0.95, ﹣0.9, ﹣0.85, ﹣0.8, 0.8, 0.85, 0.9, 0.95, 0.99) for time series length of 8, 16, 32, 64, 128, 256 respectively. The results from 10,000 simulations reveal that the models performance varies with the collinearity and autocorrelation levels, and with the time series lengths. In addition, the results reveal that the BVAR4 model is a viable model for forecasting. Therefore, we recommend that the levels of collinearity and autocorrelation, and the time series length should be considered in using an appropriate model for forecasting.

美国分类经济政策不确定性对中国省域经济的冲击效应——基于GVAR模型的实证分析

在全球经济一体化的背景下,一国经济政策不确定性会通过贸易、投资等渠道溢出到其他国家和地区,为分析美国经济不确定性对中国各省域的影响,采用全局向量自回归模型分析美国贸易、财政和货币政策不确定性对中国各省域经济的冲击效应。研究发现:美国贸易政策不确定性对各省域宏观经济的脉冲冲击大于货币政策不确定性的冲击,而财政政策不确定性的脉冲冲击明显较小;美国贸易和货币政策不确定性对各省域的脉冲冲击具有较大的异质性,省域经济发展越“充分”,对各省域消费物价指数的冲击越倾向于负向冲击,且冲击越小,而对进出口的冲击越倾向于正向冲击,且冲击越大。各省需注意贸易和货币政策不确定性对地区消费物价指数和进出口的冲击,以提前做好应对;各省域应进一步提升创新能力和开放水平,弱化外部经济政策不确定性的负向冲击。

联合大气角动量函数的VAR模型用于日长变化实时快速预报

日长变化的实时快速预报具有重要的实用价值和科学研究意义,大气角动量函数的轴向分量与日长变化有强相关性,传统预报方法对日常变化的实时快速预报精度不高。经过实验分析,联合大气角动量的向量自回归模型对日长变化进行实时快速预报方法,相对于传统的预报模型,预报精度显著提高。

基于支持向量分位数回归多期VaR测度

为实现VaR风险准确测度,考虑到波动聚集、厚尾与非对称等金融市场典型特征,基于支持向量分位数回归模型,研究了VaR及其影响因素之间的线性及非线性依赖关系,给出了多期VaR风险测度方法,并将其与传统VaR风险测度方法进行了比较.选取上证指数、香港恒生指数和标准普尔500指数进行实证研究,VaR回测检验结果表明基于支持向量分位数回归模型的多期VaR风险测度在样本内与样本外都有良好的表现.

多元自相关过程的VAR控制图

为了解决多元自相关过程的残差T^2控制图对小偏移不灵敏的问题,本文利用批量-均值法的思想,结合VAR模型的渐近分布,设计了多元自相关过程的向量自回归(VAR)控制图.只要子组样本量足够大,VAR控制图可以对过程出现的各种偏移进行有效控制.通过对比残差T^2控制图的控制效果,得出VAR控制图对小偏移灵敏、残差T^2控制图对大偏移灵敏的结论,联合使用VAR控制图和残差T^2控制图可更有效地监控多元自相关过程。

基于VAR模型的产业结构变动与农业经济增长关系研究——以安徽省为例

产业结构变动在较长时期内促进了我国工业化进程和经济增长,但随着三次产业比例和份额的调整,产业结构变动对经济增长的贡献将逐渐下降并让位于资本和技术进步,而由此引起的传统部门与现代部门之间的产业失衡则成为当前亟待解决的问题。

基于VAR模型的我国货币错配影响因素研究

发展中国家在融入全球经济体系时普遍面临货币错配，我国也不例外。近年来，我国的货币错配日益严重。在定性考察了我国货币错配影响因素的基础上，运用1985～2006年的经济数据，通过构建两个VAR模型，对我国货币错配及其影响因素进行了实证检验。结果表明：汇率制度选择对货币错配的影响最大；其次为经济增长速度；再次是贸易顺差程度。最后根据实证检验的结果，提出了管理和控制我国货币错配的四项政策建议。

基于SVAR的中国货币政策的房价传导机制

本文采用2005年7月到2009年9月宏观经济数据构建SVAR模型,分别从货币供给的利率传导机制,现金余额效应,汇率传导机制以及房地产价格对货币供给的反馈机制四个角度进行实证分析,发现:货币量的增加和汇率上升都会带来房价的大幅上涨,而利率提高所带来的房价下降程度很小,房价的上涨会引起物价和消费上涨。结论:当今房地产市场中,存在着货币政策的房价传导机制,其中利率机制对房价影响较小;在汇率机制传导过程中,中央银行为了稳定币值和升值预期引起的国际资本流入导致货币供应量被动增加,从而直接导致了房地产价格上涨。因此提出货币政策应当关注房地产价格,既要防止形成房地产价格泡沫,又要避免温水煮青蛙。

基于VAR的江西省种植业、林业、畜牧业和渔业的动态关系

利用江西省1992-2007年的种植业、林业、畜牧业、渔业产值指数,建立VAR模型,运用渐进分析法模拟的脉冲响应函数,研究江西省种植业、林业、畜牧业和渔业之间的动态关系。结果表明:种植业、林业产值不仅对来自自身的1个单位标准误差信息的反应最大,同时分别对渔业和畜牧业的冲击反应最大。而对各个行业产值与第一产业总产值之间的简单相关系数的分析也表明,在对第一产业总产值的贡献排名中,种植业排名居首位,其余排名顺序为畜牧业、渔业和林业。

货币流动性对中国农产品价格的影响——基于随机波动的TVP-VAR模型的实证分析

考察货币的流动性与农产品价格的关系是经济领域的重要课题,这涉及如何制定合适的货币政策来诱导农产品价格的合理区间,使农业生产健康稳定地可持续发展。选择2000年1月到2014年12月的月度数据,建立TVP-VAR模型,在不同经济环境下,观察货币流动性对中国农产品价格是否具有时序性特征的影响,以便增强货币政策的作用力度。主要研究结论是:农产品价格对货币流动性存在滞后性反应;农产品价格对不同来源的货币流动性在中长期中出现反方向效应;农产品价格对货币流动性的响应在不同经济状况下的作用程度不同。

经济增长、FDI与环境污染的时空传导效应研究——基于半参数空间面板VAR模型的分析

从理论上总结出经济增长、FDI及环境污染两两交互影响的研究体系,通过构建半参数空间面板VAR模型及时空脉冲响应函数分析了三者之间的冲击在时空上的传导过程。研究发现:经济大省如北京的经济增长发生冲击会对周边地区的经济增长与环境污染产生微弱但持久的正效应,同时对FDI也存在正向影响;FDI大省如江苏的FDI增长在3期内会拖累自身经济增长,长期则表现出对经济持久的拉动作用,但同时也会增大周边及自身环境污染;河北的环境污染分别在4期与3期内对经济增长与FDI有促进作用,但长期内则会抑制经济与FDI的增长。此外,还结合模型半参数部分探讨了在财政分权制度下,经济增长、FDI与环境污染的协同发展机制。

基于在线LASSO VAR和EGARCH模型的风场功率集成概率预测

由于风速波动性大,风力发电往往呈现一定的不确定性.传统风能预测模型以均值为0、方差固定的正态分布度量不确定性,但方差可能随时间变化,即具有异方差性.为提升预测精度,基于在线最小绝对收缩和选择算子的向量自回归(LASSO VAR)和指数自回归条件异方差(EGARCH)模型,提出一种考虑异方差性的风场级功率集成概率预测模型.首先使用在线LASSO VAR模型预测风力机的有功功率,再利用自回归条件异方差检验验证残差的异方差性,并利用信息冲击曲线和动态显著线评估正负残差对未来条件方差的不对称影响.然后针对异方差性和不对称性,使用EGARCH模型对单风力机有功功率的残差进行预测,得到有功功率的条件方差.最后,考虑各风力机有功功率的相关性,将风场中各风力机的有功功率求和,得到整个风场总有功功率的概率预测结果.将该方法应用于中国华东某地风场,验证了该模型能有效提高预测精度.

中国碳排放强度影响因素相关性研究——基于VAR与SVAR模型分析

本文列举出近年来研究碳排放强度的代表性文献,从中选取经济增长规模,能源强度,能源结构,产业结构四种出现频率最高的影响因素,进行四种因素对碳排放强度影响的平稳性检验,结果表明经济增长规模对碳排放强度的影响平稳性不足;对碳排放强度与能源强度、能源结构、产业结构构建结构向量自回归模型,运用脉冲响应函数分析三种因素的变化对碳排放强度的冲击效应,并用方差分解分析三种影响因素的贡献度,结果显示第三产业对碳排放强度的冲击效应最为显著,能源强度次之,能源结构最弱。