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Vol. 04, No. 07 [July 2018]

Paper Title :: A Note on Dagum Sigmoid Function with Applications to Income and Lifetime Data
Author Name :: Nikolay Kyurkchiev || Anton Iliev
Country :: Bulgaria
Page Number :: 01-05
The Dagum distribution is a flexible and simple model with applications to income, financial and lifetime data.
We prove upper and lower estimates for the Hausdorff approximation of the shifted Heaviside function by a class of Dagum cumulative distribution function – (DCDF). Numerical examples, illustrating our results are given.
Keywords:Dagum cumulative distribution function (DCDF), shifted Heaviside function, Hausdorff distance, upper and lower bounds
[1] Ch. Kleiber. A Guide to the Dagum Distribution. In: Modeling Income Distributions and Lorenz Curves: Essays in Memory of Camilo Dagum; D. Chotikapanich (ed.), Springer–Verlag, New York, 2008, ISBN 178-0-387-72796-7.
[2] C. Dagum. A new method of personal income distribution: Specification and estimation. Economie Appliquee 33, (1977), 413–437.
[3] C. Dagum. Generating systems and properties of income distribution models. Metron 38, (1980), 3–26.
[4] C. Dagum. Sistemas generadores de distribucion de ingreso y la ley de Pareto. El Trimestre Economico 47, (1980), 877–917.
[5] C. Dagum. Income distribution models. In: S. Kotz, N.L. Johnson, and C. Read (eds.): Encyclopedia of Statistical Sciences Vol.4 New York, John Wiley, 1983, 27–34.

Paper Title :: An Empirical Research on Chinese Stock Market and International Stock Market Volatility
Author Name :: Dan Qian || Wen-huiLi
Country :: China
Page Number :: 06-14
This paper selects A-share Index of Shanghai Stock Exchange, Dow Jones Industrial Average, FTSE 100, and Nikkei 225 gains from June 7, 2010 to June 6, 2018, to make an empirical research on stock market volatility based on GARCH model. The results show that there is volatility clustering, durative and leverage effects in stock market. The volatility is largely affected by the past volatility, especially in Chinese stock market. Its influence reaches 0.948. The news had an asymmetric effect on the gains, in the United States, the United Kingdom, the Japanese stock market. "bad news" have a greater impact on the gains than the equivalent "good news." In general, A-share Index of Shanghai Stock Exchange volatility is bigger than the other three. The reason is that volatility has a certain correlation with the trend of the economy. Chinese GDP growth rate has always been in a higher position, which will lead to large fluctuations in the stock market.
KeywordsShanghai A-share composite index, GARCH model, volatility
[1]. Zhang Chengsi.(2016). Econometrics: A Perspective of Time Series Analysis (Second Edition) [M]. Beijing: China Renmin University Press.
[2]. Hou Qing, Mei Qiang, Wang Juan. Research on China's Stock Market Supervision Based on Volatility Asymmetry[J]. Statistics and Decision, 2009, 064(21): 132~134.
[3]. Liu Xuan, Feng Cai. Volatility Characteristics and Asymmetric Effects of China's Stock Market——Taking the Shanghai Composite Index as an Example since the Share Reform[J].Accounting Newsletter,2010,(1):76~78.
[4]. Jiang Xiangcheng, Xiong Yamin. Research on Volatility of China's Stock Market Based on GARCH Family Model[J]. Journal of Southwest China Normal University, 2017, 42(2): 115~119.
[5]. Aggarwal, R., C. Inclan and R. Leal (1999) : “Volatility in Emerging Stock Markets ”, The Journal of Financial and QuantitativeAnalysis, Vol. 34, No. 1,33-55.

Paper Title :: An Empirical Research on Chinese Stock Market Volatility Based on Garch
Author Name :: Ya Qian Zhu || Wen huiLi
Country :: China
Page Number :: 15-23
Stock market volatility is a major issue in the modern financial field. As China's stock market is immature and volatile, it is particularly important to study the volatility of China's stock market. This paper selects Shanghai Composite Index gains from January 7, 2013 to December 29, 2017, to make an empirical research on stock market volatility based on GARCH model. The results show that there is volatility clustering, durative and leverage effects in stock market. The volatility is largely affected by the past volatility, especially in Chinese stock market. Its influence reaches 0.927.
Keywords: GARCH model, volatility, heteroscedasticity
[1]. Jiang He. Research on the Fluctuation Characteristics of Daily Return Rate of Shanghai Composite Index Based on GARCH Model[J]. Journal of Xi'an University of Finance and Economics, 2011(9): 25-29.
[2]. Chen Ying. An Empirical Analysis of the Volatility of Small and Medium-sized Board Index Based on GARCH Model[J].Management Manager.2011(19):48.
[3]. Zhou Liping. Analysis of Volatility of Shanghai and Shenzhen Stock Markets Based on GARCH Model.Economic Forum. 2017
[4]. XuXuchu, Yang Ning. Analysis of the Volatility of Stock Index Returns Based on GARCH Model. Journal of Liaocheng University.2017(12)
[5]. Gao Jinsha. Research on Volatility of China's Stock Market Based on GARCH Model. Times Finance. 2017(07)

Paper Title :: Do American Stock Market have Influence on Chinese Stock Market?
Author Name :: Ying Cheng || Wenhui Li
Country :: China
Page Number :: 24-29
Many thougt that Chinese stock market is independent, there is no related to other stock markets.We select the daily closing price of the US Standard & Poor's 500 Indexand the Shanghai Composite Index from January 5, 2015 to March 19, 2018based onVAR model, to study the interaction between US stock market and Chinese stock market.The results show that Chinese stock market is affected by the US stock market, it has 4 days lag.
Keywords: Standard & Poor's 500 Index; Shanghai Composite Index; VAR Model
[1]. Chen Xiaomeng. Research on the Spillover Effect and Joint Jump Effect of China, the United States and India Stock Market[D]. Jinan University, 2017.
[2]. Shen Hong, Xing Ying. Research on the Risk Overflow Effect of China and US Stock Market Based on CoVaR Method[J].Friends of Accounting, 2017(16):14-16.
[3]. Lin Yong, Li Yanchao. Comparative Study on Market Risk of Stock Market in Mainland China, Hong Kong, and the United States[J]. Journal of Chongqing Technology and Business University, 2017,34(03):34-38.
[4]. Zhang Weijuan. Analysis of linkage between stock market in the United States, China and Hong Kong[D]. Southwest University of Finance and Economics, 2016.
[5]. Liu Fengbo. Research on the linkage between stock market in China and the United States[D]. Dongbei University of Finance and Economics, 2016.

Paper Title :: Electrophoretic Deposition of Nickel Decorated Multi-Walled Carbon Nanotubes for Hydrogen Gas Production
Author Name :: Prem Dangi || Jaya Prasad Bhatt || Sabita Shrestha
Country :: Nepal
Page Number :: 30-34
Electrophoretic deposition (EPD) is one of the very promising techniques being developed for manipulating CNTs and other Nanomaterials. This paper reports, the EPD of Ni-decorated MWCNTs for application in hydrogen gas productions. Hydrogen production is truly green technology which is used as a sustainable fuel in future as well as it has a great advantage for its eco-friendly production. Before deposition MWCNTs were purified and surface functionalized by conc. HNO3. The oxidized MWCNTs were characterized by Fourier transfer infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). FTIR shows the presence of oxygenated functionalized group as –COOH on the surface of MWCNTs. The uniform EPD of MWCNTs is confirmed by SEM. EDS shows the presence of Nickel metal in Ni decorated MWCNTs as well as on the deposited substrates. The EPD experiments were carried out by using Ni-decorated MWCNTs on steel plate at 10V and 15V at a constant time of 6 min and a fix electrode distance of 1.5 cm. The evolution of hydrogen gas was carried out by a simple voltammetry cell via alkaline water electrolysis using 0.1M NaOH as electrolyte, where plane steel plate is used as anode and Ni-decorated MWCNTs deposited steel plate is used as cathode. The evolution of hydrogen gas at cathode and oxygen gas at anode is observed as expected. The hydrogen and oxygen was collected by downward displacement of water.
Keywords: Hydrogen production, Nickel decorated, Electrophoretic deposition
[1]. X. Wang, L. Quniqing, X. Jing, J. Zhong, W. Jinyong, L. Yan, J. Kaili and F. Shoushan, Fabrication of ultralong and electrically uniform single-walled carbon nanotubes on clean substrates,Nano Letters, 9, 2009, 3137-3141.
[2]. S. Iijima, Helical microtubes of graphitic carbon, Nature, 354, 1991, 56-58.
[3]. R. Rastogi, R. Kaushal, S.K. Tripathi, A.L. Sharma, I. Kaur and L. M. Bharadwaj, Comparative study of carbon nanotube dispersion using surfactants, Journal of Colloid Interface Science, 328, 2008, 421-428.
[4]. B.J.C. Thomas and A.R. Boccaccini, MWCNTs coating using EPD, Journal of American Ceramic Society, 88[4], 2005, 980-982.
[5]. A .R. Boccaccini, P. Karapappas, J. M. Marijuan and C. Kaya, “TiO2 coating on silicon carbide and carbon fibre substrates by electrophoretic dispersion, Journal of Material Science, 393[3], 2004, 851-857.

Paper Title :: Prediction of Concentrations of Ozone Levels in México City using Probability Distribution Functions
Author Name :: M.Sc. Zenteno Jiménez José Roberto
Country :: Mexico
Page Number :: 35-45
The study includes a data analysis from 2010 to 2017, which was proposed to obtain the best or best probability distribution functions that modeled ozone concentrations in México city, using the following pdf, exponential distribution function, gaussian reverse distribution function, normal log distribution function and gama distribution function, to obtain the estimators, the maximum verosimidity and moments method was used for the pdf range, for the estimation of the forecast model we used rmse, mse, coefficient of determination and prediction accuracy, in turn, a forecast is made for days of exceedance for this 2018 corroborating with the official air page of México City.
Keywords: Ozone, Probability Distributions Functions, Fit Indicators
[1]. A.J. Jakeman, J.A. Taylor, R.W. Simpson, Modeling distributions of air pollutant concentrations - II. Estimation of one and two parameters statistical distributions, Atmos. Environ., 20 (1986) 2435-2447.
[2]. Berger, A., Melice, J. L. and Demuth, C. L. (1982) Statistical distributions of daily and high atmospheric SO2 – concentrations. Atmospheric Environment. 16 (5), 2863 – 2877
[3]. Chhikara, R. S. and Folks, J. L. (1989) The inverse Gaussian distribution as a lifetime model. Thechnometrics. 19 (4), 461 – 468
[4]. Data base of Ozone website of México City http://www.aire.cdmx.gob.mx/
[5]. Georgopoulos, P.G. and Seinfeld, J.H. (1982) ‘Statistical distribution of air pollutant concentration’, Environmental Science Technology, Vol. 16, pp.401A–416A.

Paper Title :: Empirical Analysis of the Relationship between RMB Exchange Rate and Inflation Based on VAR Model
Author Name :: Jie-fangLiu || Ping Xiao
Country :: China
Page Number :: 46-51
With the rapid development of the economy, it is crucial to study the relationship between the RMB exchange rate and China's inflation. Based on the VAR model, this paper uses the US dollar-to-RMB exchange rate and inflation rate data from 1985 to 2017 as the research sample. Based on the data, Eviews software is used to empirically analyze the relationship between RMB exchange rate and inflation. The results show that the RMB exchange rate is the one-way Granger cause of inflation, and inflation is not the Granger cause of the RMB exchange rate. And the RMB exchange rate has a positive effect on the inflation rate. However, the impact of the RMB exchange rate on the inflation rate is relatively small, the transmission effect is low, and there is a significant time lag.
Keywords: RMB exchange rate inflation Granger test VAR model
[1]. Liu Jing. Empirical Analysis of the Impact of RMB Exchange Rate Changes on Inflation. Economic Forum, 2010(09): 51-53.
[2]. Hu Yuanqing. Wang Guibao . Discussion on the impact of RMB exchange rate on domestic inflation. Business Times, 2012 (20): 58-59.
[3]. LiuYa and Li Weiping, Yang Yujun.The impact of RMB Exchange Rate Fluctuation on China's Inflation: A Perspective of Exchange Rate Transfer Financial Research,2008(03):28-41.
[4]. Yang Wei. Dynamic Relationship between RMB Exchange Rate Changes and Inflation. Times Finance, 2011(1): 77-77.
[5]. Zhang Cheng Changes of RMB Exchange Rate and the Dynamic Trend of Inflation. International Finance Research, 2009(5): 87-96.

Paper Title :: Application of ARMA Model in China's Fixed Assets Investment Forecast
Author Name :: Ying Liu || Ping Xiao
Country :: China
Page Number :: 52-58
With the rapid development of economy, fixed assets investment as an important part of investment is one of the important driving forces of social and economic development, and an important means of social fixed assets reproduction. Therefore, predicting the growth rate of China's fixed assets investment has become an important issue and is of great significance for promoting China's economic growth. This paper uses ARMA model, uses Eviews software to conduct an in-depth analysis of China's fixed assets investment from 1980 to 2017, and predicts the fixed assets investment in China in future. It can be seen from the analysis results that the ARMA model can provide more accurate prediction effects, and can be used to predict the future data and provide a reliable basis for the fixed assets investment of the whole society in China.
Keywords: economic growth, ARMA model, fixed asset investment
[1]. Shi Meijuan, Application of ARIMA Model in the Prediction of Fixed Assets Investment in Shanghai, Statistical Education, No. 3, 2004
[2]. Zhen Baolin, He Yingdi, ARIMA model in the application of fixed assets investment forecast in Taiyuan City, Journal of Taiyuan University of Science and Technology, 2007, No.5
[3]. Li Hui, Application of ARIMA Model in China's Fixed Assets Investment Forecast[J], Heilongjiang Foreign Trade,2010, No.7
[4]. Xue Yu, based on the ARMR model: total social fixed assets investment forecast, statistics and decision-making,2014, issue 15
[5]. Li Zi Nai, Pan Wenqing, econometrics [M] (third edition). Higher Education Press.

Paper Title :: Empirical Study on Short-Term Prediction of Shanghai Composite Index Based on ARMA Model
Author Name :: Cai-xia Xiang || Ping Xiao
Country :: China
Page Number :: 59-64
This paper estimates the comprehensive forecasting model from the closing price of 370 Shanghai Composite Index from January 3, 2017 to July 10, 2018. Firstly, through the preliminary analysis of the data, the unsteady raw data is transformed to establish a new stationary time series, so as to establish the ARMA model to predict the closing price of the Shanghai Stock Exchange in the next three days. Since the Shanghai Composite Index is one of the most representative indexes in China, it can fully reflect the development trend of China's stock market to a certain extent. Therefore, it is practical significance to study the short-term changes in the daily closing price of the Shanghai Composite Index, understand the changes in the stock market, make investment decisions, and provide reliable information services and decision-making guidance for investors and decision makers.
Keywords: Time series; ARMA model; short-term forecast; Shanghai index
[1]. Liang Yan,. Xia Letian., Application of Time Series ARMA Model, Journal of Chongqing University of Technology(Natural Science),2012, (08):106-109.
[2]. Feng Pan,.Cao Xianbing,, An Empirical Study of Stock Price Analysis and Forecast Based on ARMA Model, Mathematics in Practice and Theory, 2011, (40) 22: 85-89.
[3]. Wu Chaoyang., Improved Grey Model and Stock Index Prediction of ARMA Model, Journal of Intelligent Systems, 2010, 5(3): 277-280.
[4]. Meng Kun. Li Li, An Empirical Analysis of Forecasting Stock Price Based on ARMA Model, Journal of Hebei North University(Natural Science Edition),2016,32(5).
[5]. Cong Zheng. Xu Jiaping., Research on Rural Financial Gap Calculation in Liaoning Province Based on ARMA Model, Journal of Shandong Agricultural University,2013(4).

Paper Title :: Environmental Research in China Based on Factor Analysis
Author Name :: Kun-peng XU || Ping Xiao
Country :: China
Page Number :: 65-69
This paper analyzes the advantages and disadvantages of the environment in various regions of China, and selects the environmental conditions and air quality index data of various regions in China from 2015 to 2017 for factor analysis. Among them, the use of industrial waste discharge, GDP, desertification, afforestation area and other variables on the air quality index AQI (air quality index) data, the total impact of environmental pollution control investment and PM2.5, as well as the comprehensive ranking of environmental problems in various regions. It was found that the degree of urban haze pollution was reduced and the situation was developing. The three major indicators of environmental pollution are industrial solid waste discharge, wastewater pollution, and air pollution. Among them, indicators include wastewater discharge, sulfur dioxide emissions, nitrogen oxide emissions, smoke (powder) dust, industrial solid waste production, desertification, PM2.5, regional GDP, afforestation area, total investment in environmental pollution control, and AQI.
Keywords: PM2.5; industrial pollutants; environmental pollution; AQI
[1]. Polaris Environmental Protection Network «Plan d'action de contrôle de la pollution de l'air» 2018
[2]. Polaris Environmental Network 017 Classement des concentrations PM2.5 des 365 villes chinoises 2018
[3]. Réseau de génie civil "China Environmental Problems Analysis" 2016
[4]. Réseau de génie civil "China Environmental Problems Analysis" 2016
[5]. Zhao X, Zhang X, Xu X, et al Variations saisonnières et diurnes de la concentration ambiante de PM2.5 dans les environnements urbains et ruraux à Beijing [J] Environnement atmosphérique, 2009

Paper Title :: Empirical Research on Shanghai Stock Index Based on GARCH Model
Author Name :: Cong-shu Li || Ping Xiao
Country :: China
Page Number :: 70-76
The stock market has been affected by many factors, leading to the stock market is unpredictable, which makes stocks have high-risk and high-yield characteristics. Studying the stock market's Shanghai stock Index is the key to reducing risks and increasing profits. This article analyzes the characteristics of the daily yield series by collecting the daily closing price of the Shanghai stock Index from the daily closing price of June 3, 2013 to June 29, 2018, and using Eviews statistical analysis software to analyse the nature of the sequence, the time series model GARCH(1,1) is initially fitted. The empirical results show that the GARCH(1,1) model has a good fitting effect on the time series of the logarithmic price of the Shanghai Stock Index.
Keywords: GARCH model, Shanghai stock index, volatility
[1]. Changfeng Wu. Analysis of China's Shanghai and Shenzhen Stock Markets by Using Regression-GARCH Model. Forecasting. 1999
[2]. Chaolong Yue, Empirical Research on the GARCH Model Family of Shanghai Stock Market Return Rate. Mathematics Economics and Technology Economics Research. 2001
[3]. Junbo Tang, Sixiang Yang, Shuhong He. Empirical Analysis of Shanghai Composite Index Based on GARCH Model[J]. Journal of Chongqing Technology and Business University, 2012(10).
[4]. Yanming Hou. Research on Volatility of China's Stock Market Based on ARCH Model. Jiangsu University, 2008
[5]. Engle, RF Autoregressive Conditional Heteroscedaticity with Estimates of the Variance of UK Inflation [J]. Econometrica, 1982 (50): 987-1008

Paper Title :: The impact of Internet development on the level of urbanization
Author Name :: Xin-ru Wang || Ping Xiao
Country :: China
Page Number :: 77-83
This paper analyzes the existing relationship between the two based on the Internet and urbanization, which are closely related to social development. Firstly, collect indicators that affect the level of Internet development and urbanization. Secondly, use the pearson correlation coefficient to test the correlation coefficient between the variables affecting the level of urbanization, and judge that there is a high correlation between other variables except the registered unemployment rate. Then, using SPSS to perform dimension reduction and factor analysis on the selected variables, the model is established to obtain the level of urbanization. By comparing with the urbanization rate after standardization, it is found that the data of the model is basically consistent with it. Finally, a multivariate linear regression model was established to study the relationship between the factors affecting the development of the Internet and the level of urbanization.
Keywords: Internet development ; urbanization level ; multiple linear regression; factor analysis
[1]. Ge Weimin. Network Effect---The Impact of Internet Development on the Global Economy, Shanghai Academy of Social Sciences Press, July 2004
[2]. Liu Guifang, Time and Space Analysis of Regional Differences in China's Internet, Progress in Geography, Vol. 25, No. 4, July 2006
[3]. Yu Liping, Zhou Yidong, Zhong Wei, Analysis of Factors Affecting China's Internet Development Based on PANEL DATA[J], China Soft Science, May 2007
[4]. China Internet Network Information Center. Statistical Analysis of China's Internet Development Status in 2017[R], October 2017
[5]. Wang Suzhai. Connotation Characteristics, Goals and Paths of New Urbanization Science Development[J]. Theoretical Monthly, April 2013: 165-168

Paper Title :: Human - currency exchange rate prediction based on AR model
Author Name :: Jin-yuanWang || Ping Xiao
Country :: China
Page Number :: 84-88
This paper uses the time series correlation theory to take the mid-price of the US dollar against the RMB exchange rate from January 1, 2016 to July 6, 2018 as the research sample, and establish the AR(1) model after the first-order difference of the data, against the US dollar against the RMB. The exchange rate is short-term forecasted to solve the application of import and export enterprises in import and export trade. The empirical research shows that the AR (1) model accurately predicts the exchange rate of the US dollar against the RMB, and the RMB has a small depreciation. Then it theoretically analyzes the impact of RMB depreciation on import and export enterprises, and finally puts forward some suggestions for China's import and export enterprises.
Keywords: Exchange rate, time series, AR model, import and export, suggestions
[1]. Qianjin Lu and Zhiguo Li, Decomposition of RMB real effective exchange rate and revision of Marshall-Lerner conditions, Quantitative Economics and Technology Economics Research, 30(4), 2013, 3-18.
[2]. Paul Krugman, Differences in Income Elasticities and Trends in Real Exchange Rates, European Economic Review, 33(5), 1989,1031-1046.
[3]. Rose and Andrew, Onemoney. One Market: Estimating the Effect, Common Currencies on Trade of Economic Policy, F333(4), 2000, 7432.
[4]. Yan Wang, application time series analysis(China, Mechanical Industry Press, 2013).
[5]. Shuyuan He, application time series analysis(China, Peking University Press, 2003).

Paper Title :: Analysis Factors of Affecting China's Stock Index Futures Market
Author Name :: Peng Luo || Ping Xiao
Country :: China
Page Number :: 89-94
Before the introduction of stock index futures, the trading mechanism of China's stock market has always been a single-dollar form. With the development of China's capital market, the single stock market has been unable to meet the requirements of all investors and cannot be hedged by short selling. The risk of the stock market. The introduction of stock index futures introduces a short-selling mechanism, which can well hedge risks, avoid systemic risks in the stock market, and create a better stock investment market. In particular, the emergence of the Shanghai and Shenzhen 300 stock index futures marks a new chapter in China's financial market. There are more and more new stock index futures on the market. The stock index futures market is getting more and more attention from investors. Currently, stock index futures trading has been the first variety of financial futures trading. Therefore, it is of great practical significance to empirically analyze the factors affecting China's stock index futures market.
Keywords: Stock index futures, VAR model, impulse response function
[1]. Zhang Zongcheng, Liu Shaohua.An Empirical Analysis of the Linkage and Guidance Relationship between Shanghai and Shenzhen 300 Stock Index Futures Market and Spot Market [J]. China Securities and Futures, 2010(5):4-6.
[2]. Zhang Haipeng. Cointegration Analysis of Factors Affecting China's Stock Index Futures[J]. Journal of Guizhou University of Finance and Economics,2011,29(3):42-45.
[3]. Zhang Juan, Li Ailan.An Empirical Study on the Influencing Factors of China's Stock Index Futures Price[J]. Cooperative Economy & Technology, 2013(5):62-64.
[4]. Yang Mei. Analysis of Factors Affecting China's Stock Index Futures[J].China Securities and Futures,2013(4X):24-25.
[5]. Xia Ming, Yang Chunxi.An Empirical Analysis of China's Stock Market Behavior Based on Behavioral Finance Theory [J]. Journal of Wuhan University Philosophy and Social Sciences, 2009(4):472-476.

Paper Title :: Empirical Analysis of the Relationship between CPI and PPI Based on VAR Model
Author Name :: Run-si Shu || Ping Xiao
Country :: China
Page Number :: 95-100
CPI and PPI are important economic indicators of China, and they are also an important basis for the government to formulate monetary policies and economic plans. Based on the VAR model, this paper selects the data from 1990 to 2016 using eviews software to empirically analyze the data. Through the Granger causality test, the relationship between CPI and PPI is found. PPI is the one-direction Granger reason for CPI. And CPI is not the Granger reason for PPI. Through the analysis of the impulse response function, it is found that CPI plays a role in promoting its own growth, and PPI has a positive effect on the growth of CPI.
Keywords: CPI, PPI , VAR model
[1]. Xu Guoxiang, Statistical Index Theory and Application, (Beijing: China Statistics Press, 2004).
[2]. Todd E Clark, Do Producer Prices Lead Consumer Prices, ECONIMIC REVIEW,1995(THIRD QUARTER, 25-39.
[3]. Liu Min, Zhang Yanli, Yang Yanbin ,Analysis of the Relationship between PPI and CPI, Statistical Studies 2005 (2), 24-27.
[4]. He Liping, Fan Gang, Hu Jiani, Consumer Price Index and Producer Price Index, Who drives who? Economic Research 2008 (11), 16-26.
[5]. Zhu Jianming's empirical analysis of PPI and CPI transmission mechanism Commercial Modern 2009(4).
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