With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domesti...With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.展开更多
Based on the inter-provincial panel data for 31 provinces in China from 2000 to 2019,and incorporating geospatial factors,a spatial panel vector autoregressive(SPVAR)model consisting of population mobility,industrial ...Based on the inter-provincial panel data for 31 provinces in China from 2000 to 2019,and incorporating geospatial factors,a spatial panel vector autoregressive(SPVAR)model consisting of population mobility,industrial structure upgrading,and economic growth is constructed.The space-time impulse response function is used to analyze the space-time conduction of exogenous variables on the impact of three endogenous variables.The study found that first,the population influx barely benefited the industrial structure upgrading and economic growth.Second,the upgrading of the industrial structure would aggravate the population mobility in the province,causing low-level laborers to leave the province in shortterm,but in long-term,there would be influx of talents.Third,the economic growth in developed regions plays a significant role in promoting the industrial development of their province and population-rich provinces,but it has less impact on provinces with high-level industrial structure.Finally,policy recommendations are provided in regard to the benign interaction among population mobility,industrial structure upgrading,and economic growth in addition to clarifying the idea of economic development,implementing correct population policies,and promoting the coordinated regional development.展开更多
After the outbreak of the international financial crisis,the People’s Bank of China,based on traditional monetary policy tools,launched a series of structural monetary policy tools such as standing lending facility(S...After the outbreak of the international financial crisis,the People’s Bank of China,based on traditional monetary policy tools,launched a series of structural monetary policy tools such as standing lending facility(SLF),medium-term lending facility(MLF),and pledged supplementary lending(PSL)and targeted at liquidity via the commercial banking system.In order to test the credit transmission effect of structured monetary policy,this paper empirically analyzes the relationship between structured monetary policy,bank liquidity and bank credit based on the VAR model.The research shows that the implementation of structured monetary policy reduces the liquidity of commercial banks in the short term and increases in loans to small or micro enterprises and agriculture-related loans,these policies have produced significant short-term effects on credit transmission in steady of long-term effects.Thus,a series of supporting measures are needed to fully exert the effects of structural monetary policy.展开更多
With the development of economy, more and more attention is paid to the relationship between money supply and inflation in the economic field. This paper chooses consumer price index (CPI) as an important index to mea...With the development of economy, more and more attention is paid to the relationship between money supply and inflation in the economic field. This paper chooses consumer price index (CPI) as an important index to measure the level of inflation, by choosing between January 2008 and March 2019 money in circulation M0, narrow measure M1, broad measure M2, consumer price index CPI monthly data as sample, building a vector autoregressive (VAR) model and using econometric methods of impulse response function and variance decomposition, and finally characterizes money in circulation M0, narrow measure M1, broad measure M2 and the relationship between consumer price index CPI and different sizes of the impact of inflation in the money supply relationship.展开更多
The speed of China’s economic development is gradually accelerating, and the demand for energy is also constantly increasing, especially the demand for coal. In order to reveal whether the coal imports have an impact...The speed of China’s economic development is gradually accelerating, and the demand for energy is also constantly increasing, especially the demand for coal. In order to reveal whether the coal imports have an impact on China’s economic development, this paper constructs the VAR(6) model by selecting the quarterly data of coal imports (CIV) and gross domestic product (GDP) from 2002 to 2017, performing ADF (Augmented Dickey-Fuller) stationarity test and Johansen cointegration test. It shows that there is a long-term stable equilibrium relationship between coal imports and GDP. Then the impulse response function is used to obtain the relationship between coal imports and GDP. It is found that the impact of coal imports on GDP is greater than the impact of GDP on coal imports.展开更多
Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal pric...Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.展开更多
This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(V...This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.展开更多
This study investigates the impact of online-to-offline(O2O)platforms,such as Ele.me and Meituan,on offline sales in low-frequency-high-consumption industries,specifically a mid-to-highend liquor distribution chain.Us...This study investigates the impact of online-to-offline(O2O)platforms,such as Ele.me and Meituan,on offline sales in low-frequency-high-consumption industries,specifically a mid-to-highend liquor distribution chain.Using data from 77 offline stores in Beijing collected during 2019-2022,the study employs a VAR model to analyze the relationship between offline sales and the use of O2O platforms.The results reveal a long-term equilibrium between the two,with most indicators showing a positive impact of O2O platforms on offline sales.The research provides valuable insights for lowfrequency-high-consumption enterprises in making multi-channel decisions and quantifies the impact of O2O platforms on offline sales.展开更多
The vector autoregressive （VAR） model is established with the wind velocity data from four wind observations, which are established on the Sutong Bridge reach of the Changjiang River and in Changshu, Haimen and Nant...The vector autoregressive （VAR） model is established with the wind velocity data from four wind observations, which are established on the Sutong Bridge reach of the Changjiang River and in Changshu, Haimen and Nantong meteorological observation stations. Based on the VAR model, the result of Granger causality test indicated that there is Granger causality between most of the variables. Consequently the missing wind velocity values of Sutong bridge are estimated with the condition of the wind velocity data from the other three observatories, and the result of the conditional estimation is comparatively perfect.展开更多
To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section,...To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.展开更多
Ensuring the safe operation of liquid rocket engine(LRE)systems requires reliable fault diagnosis,yet the scarcity of real fault data limits deep learning applications despite their modeling strengths.We address this ...Ensuring the safe operation of liquid rocket engine(LRE)systems requires reliable fault diagnosis,yet the scarcity of real fault data limits deep learning applications despite their modeling strengths.We address this by developing an offline detection method based on piecewise stationary vector autoregressive modeling,employing a two-phase approach that first identifies candidate change points through block fused LASSO regularization and subsequently refines them using smoothly clipped absolute deviation regularization to leverage its asymptotic unbiasedness.Validated on a high-fidelity LRE simulation dataset(26 sensors,2000 time points)with injected faults including turbopump efficiency degradation,hydrogen turbine leakage,and valve failures across 48 scenarios,our method achieves 100% precision(±50-sample tolerance)in fault timing detection without requiring training data,demonstrating superior performance to conventional autoregressive moving average models while overcoming the data dependency of neural networks.展开更多
Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for ...Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.展开更多
Four-dimensional variational(4D-VAR) data assimilation method is a perfect data assimilation solution in theory, but the computational issue is quite difficult in operational implementation.The incremental 4D-VAR assi...Four-dimensional variational(4D-VAR) data assimilation method is a perfect data assimilation solution in theory, but the computational issue is quite difficult in operational implementation.The incremental 4D-VAR assimilation scheme is set up in order to reduce the computational cost. It is shown that the accuracy of the observations, the length of the assimilation window and the choice of the first guess have an important influence on the assimilation outcome through the contrast experiment. Compared with the standard 4D-VAR assimilation scheme, the incremental 4D-VAR assimilation scheme shows its advantage in the computation speed through an assimilation experiment.展开更多
基金support from the Key Project of National Social Science Foundation of China (NO. 13&ZD159)
文摘With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.
基金This work was supported by 2021 Jiangxi University of Finance and Economics Student Innovation Training Program(No.202110421068).
文摘Based on the inter-provincial panel data for 31 provinces in China from 2000 to 2019,and incorporating geospatial factors,a spatial panel vector autoregressive(SPVAR)model consisting of population mobility,industrial structure upgrading,and economic growth is constructed.The space-time impulse response function is used to analyze the space-time conduction of exogenous variables on the impact of three endogenous variables.The study found that first,the population influx barely benefited the industrial structure upgrading and economic growth.Second,the upgrading of the industrial structure would aggravate the population mobility in the province,causing low-level laborers to leave the province in shortterm,but in long-term,there would be influx of talents.Third,the economic growth in developed regions plays a significant role in promoting the industrial development of their province and population-rich provinces,but it has less impact on provinces with high-level industrial structure.Finally,policy recommendations are provided in regard to the benign interaction among population mobility,industrial structure upgrading,and economic growth in addition to clarifying the idea of economic development,implementing correct population policies,and promoting the coordinated regional development.
文摘After the outbreak of the international financial crisis,the People’s Bank of China,based on traditional monetary policy tools,launched a series of structural monetary policy tools such as standing lending facility(SLF),medium-term lending facility(MLF),and pledged supplementary lending(PSL)and targeted at liquidity via the commercial banking system.In order to test the credit transmission effect of structured monetary policy,this paper empirically analyzes the relationship between structured monetary policy,bank liquidity and bank credit based on the VAR model.The research shows that the implementation of structured monetary policy reduces the liquidity of commercial banks in the short term and increases in loans to small or micro enterprises and agriculture-related loans,these policies have produced significant short-term effects on credit transmission in steady of long-term effects.Thus,a series of supporting measures are needed to fully exert the effects of structural monetary policy.
文摘With the development of economy, more and more attention is paid to the relationship between money supply and inflation in the economic field. This paper chooses consumer price index (CPI) as an important index to measure the level of inflation, by choosing between January 2008 and March 2019 money in circulation M0, narrow measure M1, broad measure M2, consumer price index CPI monthly data as sample, building a vector autoregressive (VAR) model and using econometric methods of impulse response function and variance decomposition, and finally characterizes money in circulation M0, narrow measure M1, broad measure M2 and the relationship between consumer price index CPI and different sizes of the impact of inflation in the money supply relationship.
文摘The speed of China’s economic development is gradually accelerating, and the demand for energy is also constantly increasing, especially the demand for coal. In order to reveal whether the coal imports have an impact on China’s economic development, this paper constructs the VAR(6) model by selecting the quarterly data of coal imports (CIV) and gross domestic product (GDP) from 2002 to 2017, performing ADF (Augmented Dickey-Fuller) stationarity test and Johansen cointegration test. It shows that there is a long-term stable equilibrium relationship between coal imports and GDP. Then the impulse response function is used to obtain the relationship between coal imports and GDP. It is found that the impact of coal imports on GDP is greater than the impact of GDP on coal imports.
基金Project(71073177)supported by the National Natural Science Foundation of ChinaProject(12JJ4077)supported by the Natural Science Foundation of Hunan Province of ChinaProject(2012zzts002)supported by the Fundamental Research Funds of Central South University,China
文摘Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.
基金supported by the research funds for Coupling Research on Industrial Upgrade and Environmental Management in the Bohai Rim-Technique,methodology,and Environmental Economic Policies(No.42076221).
文摘This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.
基金Supported by the National Natural Science Foundation of China(72172146,71772169,72272140)the Fundamental Research Funds for the Central Universities(E3E40802X2)the China Scholarship Council(202004910235)。
文摘This study investigates the impact of online-to-offline(O2O)platforms,such as Ele.me and Meituan,on offline sales in low-frequency-high-consumption industries,specifically a mid-to-highend liquor distribution chain.Using data from 77 offline stores in Beijing collected during 2019-2022,the study employs a VAR model to analyze the relationship between offline sales and the use of O2O platforms.The results reveal a long-term equilibrium between the two,with most indicators showing a positive impact of O2O platforms on offline sales.The research provides valuable insights for lowfrequency-high-consumption enterprises in making multi-channel decisions and quantifies the impact of O2O platforms on offline sales.
文摘The vector autoregressive （VAR） model is established with the wind velocity data from four wind observations, which are established on the Sutong Bridge reach of the Changjiang River and in Changshu, Haimen and Nantong meteorological observation stations. Based on the VAR model, the result of Granger causality test indicated that there is Granger causality between most of the variables. Consequently the missing wind velocity values of Sutong bridge are estimated with the condition of the wind velocity data from the other three observatories, and the result of the conditional estimation is comparatively perfect.
文摘To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.
文摘Ensuring the safe operation of liquid rocket engine(LRE)systems requires reliable fault diagnosis,yet the scarcity of real fault data limits deep learning applications despite their modeling strengths.We address this by developing an offline detection method based on piecewise stationary vector autoregressive modeling,employing a two-phase approach that first identifies candidate change points through block fused LASSO regularization and subsequently refines them using smoothly clipped absolute deviation regularization to leverage its asymptotic unbiasedness.Validated on a high-fidelity LRE simulation dataset(26 sensors,2000 time points)with injected faults including turbopump efficiency degradation,hydrogen turbine leakage,and valve failures across 48 scenarios,our method achieves 100% precision(±50-sample tolerance)in fault timing detection without requiring training data,demonstrating superior performance to conventional autoregressive moving average models while overcoming the data dependency of neural networks.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41705082, 41475101, 41690122(41690120))a Chinese Academy of Sciences Strategic Priority Project-the Western Pacific Ocean System (Grant Nos. XDA11010105 and XDA11020306)+1 种基金the National Programme on Global Change and Air–Sea Interaction (Grant Nos. GASI-IPOVAI06 and GASI-IPOVAI-01-01)the China Postdoctoral Science Foundation, and a Qingdao Postdoctoral Application Research Project
文摘Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.
基金the National Basic Research Program of China under Natural contract Nos 2007CB816001 and 2006CB400603Natinal Natural Science Foundation of China under contract Nos 40346027 and 40676008the China"908"-Project under Grant No.908-02-01-03 and 908-IC-I-13
文摘Four-dimensional variational(4D-VAR) data assimilation method is a perfect data assimilation solution in theory, but the computational issue is quite difficult in operational implementation.The incremental 4D-VAR assimilation scheme is set up in order to reduce the computational cost. It is shown that the accuracy of the observations, the length of the assimilation window and the choice of the first guess have an important influence on the assimilation outcome through the contrast experiment. Compared with the standard 4D-VAR assimilation scheme, the incremental 4D-VAR assimilation scheme shows its advantage in the computation speed through an assimilation experiment.
